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GSQL Language Reference Defining Graphs and Loading Data

Version 2.0 to 2.1

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Introduction

The GSQL™ software program is the TigerGraph comprehensive environment for designing graph schemas, loading and managing data to build a graph, and querying the graph to perform data analysis.  In short, TigerGraph users do most of their work via the GSQL program. This document presents the syntax and features of the GSQL language.

This document is a reference manual, not a tutorial. The user should read



GSQL Demo Examples v2.1



prior to using this document. There are also User Guides or Tutorials for particular aspects of the GSQL environment. This document is best used when the reader already has some basic familiarity with running GSQL and then wants a more detailed understanding of a particular topic.

This document is Part 1 of the GSQL Language Reference, which describes system basics, defining a graph schema, and loading data.  Part 2 describes querying.

A handy GSQL Reference Card
lists the syntax for the most commonly used GSQL commands for graph definition and data loading
. Look for the reference card on our User Document home page.


GSQL Workflow

The GSQL workflow has four major steps:

  1. Define a graph schema or model.
  2. Load data into the TigerGraph system.
  3. Create and install queries.
  4. Run queries.

After initial data and queries have been installed, the user can run queries or go back to load more data and create additional queries. This document provides specifications and details for steps 1 and 2. The Appendix contains flowcharts which provide a visual understanding of the required and allowed sequence of commands to proceed through the workflow.


Language Basics


  • Identifiers
     

    Identifiers are user-defined names. An identifier consists of letters, digits, and the underscore.  Identifiers may not begin with a digit.  Identifiers are case sensitive.


  • Keywords and Reserved Words
     

    Keywords are words with a predefined semantic meaning in the language. Keywords are not case sensitive. Reserved words are set aside for use by the language, either now or in the future. Reserved words may not be reused as user-defined identifiers.  In most cases, a keyword is also a reserved word.  For example, VERTEX is a keyword.  It is also a reserved word, so VERTEX may not be used as an identifier. 

     


  • Statements
     

    Each line corresponds to one statement (except in multi-line mode). Usually, there is no punctuation at the end of a top-level statement. Some statements, such as CREATE LOADING JOB, are block statements which enclose a set of statements within themselves. Some punctuation may be needed to separate the statements within a block.


  • Comments
    Within a command file, comments are text that is ignored by the language interpreter.

    Single line comments begin with either # or //.
    A comment may be on the same line with interpreted code
    . Text to the left of the comment marker is interpreted, and text to the right of the marker is ignored.

    Multi-line comment blocks begin with /* and end with */


Documentation Notation

In the documentation, code examples are either

template code

(formally describing the syntax of part of the language) or

actual code


examples

.  Actual code examples show code that can be run exactly as shown, e.g., copy-and-paste. Template code, on the other hand, cannot be run exactly as shown because it uses placeholder names and additional symbols to explain the syntax. It should be clear from context whether an example is template code or actual code.

This guide uses conventional notation for software documentation.  In particular, note the following:


  • Shell prompts

    Most of the examples in this document take place within the GSQL shell.  When clarity is needed, the GSQL shell prompt is represented by a greater-than arrow:

    >


    When a command is to be issued from the operating system, outside of the GSQL shell, the prompt is the following:

    os$


  • Keywords

    In the GSQL language, keywords are not case sensitive, but user-defined identifiers are case sensitive. In code examples, keywords are in ALL CAPS to make clear the distinction between keywords and user-defined identifiers.


    In a very few cases, some option keywords are case-sensitive. For example, in the command to delete all data from the graph store,

    clear graph store -HARD

    the option -HARD must be in all capital letters.

     


  • Placeholder identifiers and values
     

    In template code, any token that is not a keyword, a literal value, or punctuation is a placeholder identifier or a placeholder value.Example:


    CREATE UNDIRECTED EDGE

    edge_type_name

    (FROM

    vertex_type_name1

    , TO

    vertex_type_name2

    ,





    attribute_name

    type [DEFAULT

    default_value

    ],...)

    The user-defined identifiers are

    edge_type_

    n

    ame
    , vertex_type_name1, vertex_type_name2, attribute_name

    and

    default_value

    . As explained in the Create Vertex section,


    type


    is one of the attribute data types.


  • Quotation Marks
     

    When quotation marks are shown, they are to be typed as shown (unless stated otherwise). A placeholder for a string value will not have quotation marks in the template code, but if a template is converted to actual code, quotation marks should be used around string values.


  • Choices
     

    The vertical bar | is used to separate the choices, when the syntax requires that the user choose one out of a set of values. Example:  Either the keyword

    VERTEX

    or

    EDGE

    is to be used. Also, note the inclusion of quotation marks.Template:


    LOAD


    "

    file_path

    "


    TO VERTEX|EDGE

    object_type_name

    VALUES (id_expr,

    attr_expr1

    ,

    attr_expr2

    ,...)

    Possible actual values:


    LOAD


    "data/users.csv"


    TO VERTEX user VALUES ($0, $1, $2)



  • Optional content
     

    Square brackets are used to enclose a portion that is optional.  Options can be nested. Square brackets themselves are rarely used as part of the GSQL language itself.Example: In the RUN JOB statement, the -n flag is optional.  If used, -n is to be followed by a value.


    RUN JOB [-n

    count

    ]

    job_name

    Sometimes, options are nested, which means that an inner option can only be used if the outer option is used:


    RUN JOB [-n [

    first_line_num

    , ]

    last_line_num

    ]

    job_name

    means that

    first_line_num

    may be specified if and only if

    last_line_num

    is specified first. These options provide three possible forms for this statement:


    RUN JOB

    job_name

    RUN JOB -n

    last_line_num


    job_name


    RUN JOB -n

    first_line_num

    ,

    last_line_num


    job_name


  • Repeated zero or more times
     

    In template code, it is sometimes desirable to show that a term is repeated an arbitrary number of times. For example, a vertex definition contains zero or more user-defined attributes. A loading job contains one or more LOAD statements. In formal template code, if an asterisk (Kleene star) immediately follows option brackets, then the bracketed term can be repeated zero or more times.  For example:


    TO VERTEX|EDGE

    object_name

    VALUES (

    id_expr

    [,

    attr_expr

    ]*)

    means that the VALUES list contains at least one attribute expression. It may be followed by any number of additional attribute expressions. Each additional attribute expression must be preceded by a comma.


  • Long lines
    For more convenient display, long statements in this guide may sometimes be displayed on multiple lines.  This is for display purposes only; the actual code must be entered as a single line (unless the multiline mode is used).  When necessary, the examples may show a shell prompt before the start of a statement, to clearly mark where each statement begins.

    Example: A SELECT query is grammatically a single statement, so GSQL requires that it be entered as a single line.


    Long statement displayed as one line
    SELECT *|attribute_name FROM vertex_type_name [WHERE conditions] [ORDER BY attribute1,attribute2,...] [LIMIT k]

    However, the statement is easier to read and to understand when displayed one clause per line:


    Long statement displayed on multiple lines but with only one prompt
    > SELECT *|attribute_name 
        FROM vertex_type_name
        [WHERE conditions]
        [ORDER BY attribute1,attribute2,...]
        [LIMIT k]


System and Language Basics


Running GSQL




New -g Option to set the working graph

To enter the GSQL shell and work in interactive mode, type


gsql


from an operating system shell prompt. A user name, password, and a graph name may also be provided on the command line.


GSQL command syntax for entering interactive mode
gsql [-u username] [-p password] [-g gname]

If a user name if provided but not a password, the GSQL system will then ask for the user’s password:


Login example with user name
os$ gsql -u victor
Password for victor : ***
GSQL >

If a user name is not given, then GSQL will assume that you are attempting to log in as the default tigergraph user:


Login example without user name
os$ gsql
Password for tigergraph : *****
GSQL >

 

To exit the GSQL shell, type either


exit


or


quit


at the GSQL prompt:



GSQL>




EXIT

or



GSQL>


QUIT

Multiple Shell Sessions

Multiple shell sessions of GSQL may be run at the same time.  This feature can be used to have multiple clients (human or machine) using the system to perform concurrent operations. A basic locking scheme is used to maintain isolation and consistency.

Multi-line Mode – BEGIN, END, ABORT

In interactive mode, the default behavior is to treat each line as one statement; the GSQL interpreter will activate as soon as the End-Of-Line character is entered.

Multi-line mode allows the user to enter several lines of text without triggering immediate execution.  This is useful when a statement is very long and the user would like to split it into multiple lines. It is also useful when defining a JOB, because jobs typically contain multiple statements.

To enter multi-line mode, use the command BEGIN.  The end-of-line character is now disabled from triggering execution.  The shell remains in multi-line mode until the command END is entered.  The END command also triggers the execution of the multi-line block.  In the example below, BEGIN and END are used to allow the SELECT statement to be split into several lines:


Example: BEGIN and END defining a multi-line block
BEGIN
SELECT member_id, last_name, first_name, date_joined, status
FROM Member
WHERE age >= 21
ORDER BY last_name, first_name
END

Alternately, the ABORT command exits multi-line mode and discards the multi-line block.


Command Files and Inline Commands

A command file is a text file containing a series of GSQL statements.  Blank lines and comments are ignored. By convention, GSQL command files end with the suffix .


gsql


, but this is not a requirement. Command files are automatically treated as multi-line mode, so BEGIN and END statements are not needed. Command files may be run either from within the GSQL shell by prefixing the filename with an @ symbol:



GSQL>


@file.gsl

or from the operating system (i.e., a Linux shell) by giving the filename as the argument after gsql:



os$


gsql file.gsql

 

Similarly, a single GSQL command can be run by enclosing the command string in quotation marks and placing it at the end of the GSQL statement.  Either single or double quotation marks.  It is recommended to use single quotation marks to enclose the entire command and double quotation marks to enclose any strings within the command.


Login example with inline command or command file
gsql [-u username] [-g graphname] [‘command_string’ | command_file]

In the example below, the file name_query.gsql contains the multi-line CREATE QUERY block to define the query namesSimilar.


Example using command files and inline commands
os$ gsql pagerank_query.gsql
os$ gsql ‘INSTALL QUERY namesSimilar’
os$ gsql ‘RUN QUERY namesSimilar (0,”michael”,”jackson”,100)’


Help and Information


The


help


command displays a summary of the available GSQL commands:




GSQL>

HELP [BASIC|QUERY]


Note that the HELP command has options for showing more details about certain categories of commands.


The


ls


command displays the

catalog

: all the vertex types, edge types, graphs, queries, jobs, and session parameters which have been defined by the user.

–reset option

The –reset option will clear the entire graph data store and erase all related definitions (graph schema, loading jobs, and queries) from the Dictionary.  The data deletion cannot be undone; use with extreme caution. The REST++, GPE, and GSE modules will be turned off.

$ gsql –reset

Resetting the catalog.

Shutdown restpp gse gpe …
Graph store /home/tigergraph/tigergraph/gstore/0/ has been cleared!
The catalog was reset and the graph store was cleared.

Summary

The table below summaries the basic system commands introduced so far.

Command Description
HELP [BASIC|QUERY]
Display the help menu for all or a subset of the commands
LS

Display the catalog, which records all the vertex types, edge types, graphs, queries, jobs, and session parameters that have been defined for the current active graph.




See notes below concerning graph- and role-dependent visibility of the catalog.

BEGIN
Enter multi-line edit mode (only for console mode within the shell)
END
Finish multi-line edit mode and execute the multi-line block.
ABORT
Abort multi-line edit mode and discard the multi-line block.
@file.gsql
Run the gsql statements in the command file

file.gsql

from within the GSQL shell.


os$


gsql file.gsql

Run the gsql statements in the command file

file.gsql

from an operating system shell.


os$


gsql 'command_string'

Run a single gsql statement from the operating system shell.


os$

gsql --reset
Clear the graph store and erase the dictionary.

Notes on the LS command





Starting with v1.2, the output of the LS command is sensitive to the user and the active graph:

  1. If the user has not set an active graph or specified “USE GLOBAL”:
    1. If the user is a superuser, then LS displays global vertices, global edges, and all graph schemas.
    2. If the user is not a superuser, then LS displays nothing (null).
  2. If the user has set an active graph, then LS displays the schema, jobs, queries, and other definitions for that particular graph.


Session Parameters

Session parameters are built-in system variables whose values are valid during the current session; their values do not endure after the session ends. In interactive command mode, a session starts and ends when entering and exiting interactive mode, respectively. When running a command file, the session lasts during the execution of the command file.

Use the SET command to set the value of a session parameter:

SET session_parameter = value

 

Session Parameter Meaning and Usage
sys.data_root The value should be a string, representing the absolute or relative path to the folder where data files are stored. After the parameter has been set, a loading statement can reference this parameter with $sys.data_root.
gsql_src_dir The value should be a string, representing the absolute or relative path to the root folder for the gsql system installation. After the parameter has been set, a loading statement can reference this parameter with $gsql_src_dir.
exit_on_error When this parameter is true (default), if a semantic error occurs while running a GSQL command file, the GSQL shell will terminate. Accepted parameter values: true, false (case insensitive). If the parameter is set to false, then a command file which is syntactically correct will continue running, even if certain runtime errors in individual commands occur. Specifically, this affects these commands:

  • CREATE
  • INSTALL QUERY
  • RUN JOB

Semantic errors include a reference to a nonexistent entity or an improper reuse of an entity.

This session parameter does not affect GSQL interactive mode; GSQL interactive mode does not exit on any error.

This
session
parameter does not affect syntactic errors: GSQL will always exit on a syntactic error.


Example of exit_on_error = FALSE
# exitOnError.gsql
SET exit_on_error = FALSECREATE VERTEX v(PRIMARY_ID id INT, name STRING)
CREATE VERTEX v(PRIMARY_ID id INT, weight FLOAT) #error 1: can’t define VERTEX v

CREATE UNDIRECTED EDGE e2 (FROM u, TO v) #error 2: vertex type u doesn’t exist
CREATE UNDIRECTED EDGE e1 (FROM v, TO v)

CREATE GRAPH g(v) #error 3: no graph definition has no edge type
CREATE GRAPH g2(*)


Results

os$ gsql exitOnError.gsql

The vertex type v is created.
Semantic Check Fails: The vertex name v is used by another object! Please use a different name.
failed to create the vertex type v
Semantic Check Fails: FROM or TO vertex type does not exist!
failed to create the edge type e2
The edge type e1 is created.
Semantic Check Fails: There is no edge type specified! Please specify at least one edge type!
The graph g could not be created!

Restarting gse gpe restpp …

Finish restarting services in 11.955 seconds!
The graph g2 is created.

Attribute Data Types

Each attribute of a vertex or edge has an assigned data type. The following types are currently supported.

Primitive Types

name default value valid input format (regex) Range and Precision description
INT 0 [-+]?[0-9]+
from –2

63

to +2

63


– 1

(-9,223,372,036,854,775,808 to 9,223,372,036,854,775,807)
8-byte signed integer
UINT 0 [0-9]+ from 0 to

2

64

– 1 (18,446,744,073,709,551,615)
8-byte unsigned integer
FLOAT 0.0
[


-+


]


?


[


0





9


]


*


\.


?


[


0





9


]


+


(


[


eE


]


[


-+


]


?


[


0





9


]


+


)


?
+/- 3.4 E +/-38, 7 bits of precision

4-byte single-precision floating point number

Examples: 3.14159, .0065e14, 7E23

See note below.

DOUBLE 0.0
[


-+


]


?


[


0





9


]


*


\.


?


[


0





9


]


+


(


[


eE


]


[


-+


]


?


[


0





9


]


+


)


?
+/- 1.7 E +/-308, 15 bits of precision

8-byte double-precision floating point number.

Has the same input and output format as FLOAT, but the range and precision are greater. See note below.

BOOL false “true”, “false” (case insensitive), 1, 0 true, false boolean true and false, represented within GSQL as

true

and

false

, and represented in input and output as 1 and 0
STRING empty string .* UTF-8 character string. The string value can optionally be enclosed by single quote marks or double quote marks. Please see the QUOTE parameter in Section “Other Optional LOAD Clauses”.

For FLOAT and DOUBLE values, the GSQL Loader supports exponential notation as shown (e.g., 1.25 E-7).

The GSQL Query Language currently only reads values without exponents. It may display output values with exponential notation, however.


Some numeric expressions may return a non-numeric string result, such as “inf” for Infinity or “NaN” for Not a Number.

Advanced Types

name default value supported data format Range and Precision description
STRING COMPRESS empty string .* UTF-8 string with a finite set of categorical values. The GSQL system uses dictionary encoding to assign a unique integer to each new string value, and then to store the values as integers.
DATETIME UTC time 0 see Section ”
Loading DATETIME Attribute
1582-10-15 00:00:00 to 9999-12-31 23:59:59 date and time (UTC) as the number of seconds elapsed since the start of Jan 1, 1970. Time zones are not supported. Displayed in YYYY-MM-DD hh:mm:ss format.
FIXED_BINARY(

n

)
N/A N/A stream of n binary-encoded bytes

Additionally, GSQL also support following complex data types:

Complex Types


  • User Defined Tuple (UDT)

    : UDT represents an ordered structure of several fields of same or different types. The supported field types are listed below. Each field in a UDT has a fixed size. A STRING field must be given a size in characters, and the loader will only load the first given number of characters. A INT or UINT field can optionally be given a size in bytes.

    Field Type User-specified size? Size Choices (in Byte, except STRING) Range (N is size)
    INT optional 1, 2, 4 (default), 8 0 to 2^(N*8) – 1
    UINT optional 1, 2, 4 (default), 8 -2^(N*8 – 1) to 2^(N*8 – 1) – 1
    FLOAT no same as FLOAT attribute
    DOUBLE no same as DOUBLE attribute
    DATETIME no same as DATETIME attribute
    BOOL no true, false
    STRING required Any number of characters Any string in N characters

    Below is an example of defining a UDT:


    Example of a UDT
    TYPEDEF TUPLE <field1 INT (1), field2 UINT, field3 STRING (10), field4 DOUBLE > myTuple

    In this example, myTuple is the name of this UDT. It contains four fields: a 1-byte INT field named field1, a 4-byte UINT field named field2, a 10-character STRING field named field3, and a (8-byte) DOUBLE field named field4.


  • LIST/SET

    : A set is a

    unordered

    collection of unique elements of the same type; A list is an

    ordered

    collection of elements of the same type. A list can contain duplicate elements; a set cannot. The default value of either is an empty list/set. The supported element types of a list or a set are INT, DOUBLE, STRING, STRING COMPRESS, DATETIME, and UDT. To declare a list or set type, use <> brackets to enclose the element type, e.g.,SET<INT>, LIST<STRING COMPRESS>.


    Due to multithreaded GSQL loading, the initial order of elements loaded into a LIST might be different than the order in which they appeared in the input data.


  • MAP

    : A map is a collection of key-value pairs. It cannot contain duplicate keys, and each key maps to one value. The default value is an empty map. The supported key types are INT, STRING, STRING COMPRESS, and DATETIME. The supported value types are INT, DOUBLE, STRING, STRING COMPRESS, DATETIME, and UDT. To declare a map type, use <> to enclose the types, with a comma to separate the key and value types, e.g.,MAP<INT, DOUBLE>.


Defining a Graph Schema

Before data can be loaded into the graph store, the user must define a


graph schema.


A graph schema is a “dictionary” that defines the types of entities,


vertices


and


edges


, in the graph and how those types of entities are related to one another. In the figure below, circles represent vertex types, and lines represent edge types. The labeling text shows the name of each type. This example has four types of vertices:

User, Occupation, Book,

and

Genre

.  Also, the example has 3 types of edges:

user_occupation, user_book_rating,

and

book_genre

. Note that this diagram does not say anything about how many users or books are in the graph database.  It also does not indicate the cardinality of the relationship. For example, it does not specify whether a User may connect to multiple occupations.

An edge connects two vertices; in TigerGraph terminology these two vertices are the


source vertex


and the


target vertex


. An edge type can be either


directed


or


undirected


.  A directed edge has a clear semantic direction, from the source vertex to the target vertex. For example, if there is an edge type that represents a plane flight segment, each segment needs to distinguish which airport is the origin (source vertex) and which airport is the destination (target vertex).  In the example schema below, all of the edges are undirected. A useful test to decide whether an edge should be directed or undirected is the following: “An edge type is directed if knowing there is a relationship from A to B does not tell me whether there is a relationship from B to A.” Having nonstop service from Chicago to Shanghai does not automatically imply there is nonstop service from Shanghai to Chicago.




Figure 1

– A schema for a User-Book-Rating graph

An expanded schema is shown below, containing all the original vertex and edge types plus three additional edge types:

friend_of, sequel_of, and user_book_read

. Note that

friend_of

joins a User to a User. The friendship is assumed to be bidirectional, so the edge type is undirected.

Sequel_of

joins a Book to a Book but it is directed, as evidenced by the arrowhead.

The Two Towers

is the sequel of

The Fellowship of the Ring

, but the reverse is not true. User_book_read is added to illustrate that there may be more than one edge type between a pair of vertex types.




Figure 2

– Expanded-User-Book-Rating schema with additional edges

The TigerGraph system user designs a graph schema to fit the source data and the user’s needs and interests. The TigerGraph system user should consider what type of relationships are of interest and what type of analysis is needed. The TigerGraph system lets the user modify an existing schema, so the user is not locked into the initial design decision.

In the first schema diagram above, there are seven entities: four vertex types and three edge types.You may wonder why it was decided to make Occupation a separate vertex type instead of an attribute of User. Likewise, why is Genre a vertex type instead of an attribute of Book?  These are examples of design choices.  Occupation and Genre were separated out as vertex types because in graph analysis, if an attribute will be used as a query variable, it is often easier to work with as a vertex type.

Once the graph designer has chosen a graph schema, the schema is ready to be formalized into a series of GSQL statements.

Graph Creation and Modification Privileges





Only superusers can define global vertex types. global edge types, and graphs, using CREATE VERTEX / EDGE / GRAPH.

However, once a graph has been created, its admin and designers users can customize its schema, including adding new local vertex types and local edge types, by using a SCHEMA_CHANGE JOB, described in the next section.


CREATE VERTEX




Available to superusers only.

The CREATE VERTEX statement defines a new global vertex type, with a name and an attribute list.  At a high level of abstraction, the format is



CREATE VERTEX

vertex_type_name

(PRIMARY_ID id type [,attribute_list]) [

vertex_options

]

More specifically, the syntax is as follows:


CREATE VERTEX Syntax
CREATE VERTEX vertex_type_name (PRIMARY_ID id_name type
[, attribute_name type [DEFAULT default_value] ]*)
[WITH STATS=”none”|”outdegree_by_edgetype”]

 


PRIMARY_ID

The primary_id is a required field whose purpose is to uniquely identify each vertex instance. GSQL creates a hash index on the primary id with O(1) time complexity. Only two data types are permitted, either STRING or UINT.


Vertex Attribute List

The attribute li
st, enclosed in parentheses, is a list of one or more


id definitions


and


attribute descriptions


separated by commas:


(PRIMARY_ID

id_name


type

,

[

attribute_name


type

[DEFAULT

default_value

] ]*)

    1. The first item is required to be

      PRIMARY_ID

      id


      type


      , where



      id



      is an identifier, and



      type



      is one of the attribute data types listed in the “Language Basics” section.
    2. Discontinued Feature


      The NULL and NOT NULL properties are not supported. NULL is not a supported value in the graph database.

      If an input value is not specified for an attribute when a vertex or edge instance is being created, then the attribute will have the default value for that data type.


    3. Every attribute data type has a built-in default value (e.g., the default value for INT type is 0). The

      DEFAULT default_value

      option overrides the built-in value.
    4. Any number of additional attributes may be listed after the id attribute. Each attribute has a name, type, and optional default value (for primitive-type, DATETIME, or STRING COMPRESS attributes only)


Example:

  • Create vertex types for the graph schema of Figure 1.

    Vertex definitions for User-Book-Rating graph
    CREATE VERTEX User (PRIMARY_ID user_id UINT, name STRING, age UINT, gender STRING, postalCode STRING)
    CREATE VERTEX Occupation (PRIMARY_ID occ_id UINT, occ_name STRING)
    WITH STATS=”outdegree_by_edgetype”
    CREATE VERTEX Book (PRIMARY_ID bookcode UINT, title STRING, pub_year UINT)
    WITH STATS=”none”
    CREATE VERTEX Genre (PRIMARY_ID genre_id STRING, genre_name STRING)

    Unlike the tables in a relational database, vertex types do not need to have a foreign key attribute for one vertex type to have a relationship to another vertex type.  Such relationships are handled by edge types.


WITH STATS

By default, when the loader stores a vertex and its attributes in the graph store, it also stores some statistics about the vertex’s outdegree – how many connections it has to other vertices.  The optional WITH STATS clause lets the user control how much information is recorded. Recording the information in the graph store will speed up queries which need degree information, but it increases the memory usage.  There are two* options. If “outdegree_by_edgetype” is chosen, then each vertex records a list of degree count values, one value for each type of edge in the schema. If “none” is chosen, then no degree statistics are recorded with each vertex. If the WITH STATS clause is not used, the loader acts as if “outdegree_by_edgetype” were selected.


*As for v1.1, The option “outdegree” is not longer available.

Example

:If outdegree information is recorded, it can be retrieved in a query using the vertex’s outdegree() function.

The graph below has two types of edges between persons: phone_call and text.  For Bobby, the “outdegree_by_edgetype” option records how many phone calls Bobby made (1) and how many text messages Bobby sent (2). This information can be retreived using the built-in vertex function outdegree().  To get the outdegree of a specific edge type, provide the edgetype name as a string parameter.  To get the total outdegree, omit the parameter.




Figure 3

– Outdegree stats illustration

WITH STATS option (case insensitive) Bobby.outdegree() Bobby.outdegree(“text”) Bobby.outdegree(“phone_call”)
“none” not available not available not available
“outdegree_by_edgetype”

(default)

3 2 1


CREATE EDGE




Available only to superusers.

The CREATE EDGE statement defines a new global edge type. There are two forms of the CREATE EDGE statement, one for directed edges and one for undirected edges.  Each edge type must specify that it connects FROM one vertex type TO another vertex type.  Additional attributes may be added.  Each attribute follows the same requirements as described in the


Attribute List subsection for the “CREATE VERTEX” section.


CREATE UNDIRECTED EDGE
CREATE UNDIRECTED EDGE edge_type_name (FROM vertex_type_name, TO vertex_type_name
[, attribute_name type [DEFAULT default_value]]* )

 


CREATE DIRECTED EDGE
CREATE DIRECTED EDGE edge_type_name (FROM vertex_type_name, TO vertex_type_name
[, attribute_name type [DEFAULT default_value]]* )
[WITH REVERSE_EDGE=”rev_name”]

 

Viewed at a higher level of abstraction, the format is



CREATE UNDIRECTED|DIRECTED EDGE

edge_type_name

(FROM

vertex_type_name

, TO

vertex_type_name

,


edge_attribute_list

) [

edge_options

]
Note that edges do not have a PRIMARY_ID field. Instead, each edge is uniquely identified by a FROM vertex, a TO vertex, and optionally other attributes.  The edge type may also be a distinguishing characteristic. For example, as shown in Figure 2 above, there are two types of edges between User and Book.  Therefore, both types would have attribute lists which begin

(FROM User, To Book,...).

Discontinued Feature


The NULL and NOT NULL properties are not supported. NULL is not a supported value in the graph database.

An edge type can be defined which connects FROM any type of vertex and/or TO any type of vertex.  Use the wildcard symbol * to indicate “any vertex type”. For example, the any_edge type below can connect from any vertex to any other vertex:


Wildcard edge type
CREATE DIRECTED EDGE any_edge (FROM *, TO *, label STRING)

 

WITH REVERSE_EDGE

If a CREATE DIRECTED EDGE statement includes the WITH REVERSE_EDGE=”

rev_name

” optional clause, then an additional directed edge type called ”


rev_name


” is automatically created, with the FROM and TO vertices swapped.  Moreover, whenever a new edge is created, a reverse edge is also created. The reverse edge will have the same attributes, and whenever the principal edge is updated, the corresponding reverse edge is also updated.

In a TigerGraph system, reverse edges provide the most efficient way to perform graph queries and searches that need to look “backwards”. For example, referring to the schema of Figure 2, the query “What is the sequel of Book X, if it has one?” is a forward search, using

sequel_of

edges.  However, the query “Is Book X a sequel? If so, what Book came before X?” requires examining reverse edges.


Example:

Create undirected edges for the three edge types in Figure 1.


Edge definitions for User-Book-Rating graph
CREATE UNDIRECTED EDGE user_occupation (FROM User, TO Occupation)
CREATE UNDIRECTED EDGE book_genre (FROM Book, TO Genre)
CREATE UNDIRECTED EDGE user_book_rating (FROM User, TO Book, rating UINT, date_time UINT)

The


user_occupation


and


book_genre


edges have no attributes. A


user_book_rating


edge


symbolizes that a user has assigned a rating to a book. Therefore it  includes an additional attribute


rating


. In this case the


rating


attribute is defined to be an integer, but it could just as easily have been set to be a float attribute.


Example

:

Create the additional edges depicted in Figure 2.


Additional Edge definitions for Expanded-User-Book-Rating graph
CREATE UNDIRECTED EDGE friend_of (FROM User, TO User, on_date UINT)
CREATE UNDIRECTED EDGE user_book_read (FROM User, To Book, on_date UINT)
CREATE DIRECTED EDGE sequel_of (FROM Book, TO Book) WITH REVERSE_EDGE=”preceded_by”

Every time the GSQL loader creates a


sequel_of


edge, it will also automatically create a


preceded_by


edge, pointing in the opposite direction.


Special Options


Sharing a Compression Dictionary

The STRING COMPRESS and STRING_SET COMPRESS data types achieve compression by mapping each unique attribute value to a small integer. The mapping table (“this string” = “this integer”) is called the dictionary. If two such attributes have the same or similar sets of possible values, then it is desirable to have them share one dictionary because it uses less storage space.

When a STRING COMPRESS attribute is declared in a vertex or edge, the user can optionally provide a name for the dictionary. Any attributes which share the same dictionary name will share the same dictionary. For example, v1.attr1, v1.attr2, and e.attr1 below share the same dictionary named “e1”.


Shared STRING COMPRESS dictionaries
CREATE VERTEX v1 (PRIMARY_ID main_id STRING, att1 STRING COMPRESS e1, att2 STRING COMPRESS e1)
CREATE UNDIRECTED EDGE e (FROM v1, TO v2, att1 STRING COMPRESS e1)


CREATE GRAPH

Multiple Graph support





Available only to superusers.

If the optional MultiGraph service is enabled, CREATE GRAPH can be invoked multiple times to define multiple graphs, and vertex types and edge types may be re-used (shared) among multiple graphs. There is an option to assign an admin use for the new graph.

After all the required vertex and edge types are created, the CREATE GRAPH command defines a graph schema which contains the given vertex types and edge types, and prepares the graph store to accept data. The vertex types and edge types may be listed in any order.


CREATE GRAPH syntax
CREATE GRAPH gname (vertex_or_edge_type, vertex_or_edge_type…) [WITH ADMIN username]

The optional WITH ADMIN clause sets the named user to be the admin for the new graph.

As a convenience, executing CREATE GRAPH will set the new graph to be the working graph.

Instead of providing a list of specific vertex types and edge types, it is also possible to define a graph type which includes all the available vertex types and edge types. It is also legal to create a graph with an empty domain.  A SCHEMA_CHANGE can be used later to add vertex and edge types.


Examples of CREATE GRAPH with all vertex & edge types and with an empty domain.
CREATE GRAPH everythingGraph (*)
CREATE GRAPH emptyGraph ()

 


Examples

:

Create graph

Book_rating

for the edge and vertex types defined for Figure 1:


Graph definition for User-Book-Rating graph
CREATE GRAPH Book_rating (*)

 

The following code example shows the full set of statements to define the expanded user-book-rating graph:


Full definition for the Expanded User-Book-Rating graph
CREATE VERTEX User (PRIMARY_ID user_id UINT, name STRING, age UINT, gender STRING, postalCode STRING)
CREATE VERTEX Occupation (PRIMARY_ID occ_id UINT, occ_name STRING)
WITH STATS=”outdegree_by_edgetype”
CREATE VERTEX Book (PRIMARY_ID bookcode UINT, title STRING, pub_year UINT)
WITH STATS=”none”
CREATE VERTEX Genre (PRIMARY_ID genre_id STRING, genre_name STRING)
CREATE UNDIRECTED EDGE user_occupation (FROM User, TO Occupation)
CREATE UNDIRECTED EDGE book_genre (FROM Book, TO Genre)
CREATE UNDIRECTED EDGE user_book_rating (FROM User, TO Book, rating UINT, date_time UINT)
CREATE UNDIRECTED EDGE friend_of (FROM User, TO User, on_date UINT)
CREATE UNDIRECTED EDGE user_book_read (FROM User, To Book, on_date UINT)
CREATE DIRECTED EDGE sequel_of (FROM Book, TO Book) WITH REVERSE_EDGE=”preceded_by”
CREATE GRAPH Book_rating (*)


USE GRAPH




New requirement for MultiGraph support. Applies even if only one graph exists.

Before a user can make use of a graph, first the user must be granted a role on that graph by an admin user of that graph or by a superuser. (Superusers are automatically granted the admin role on every graph). Second, for each GSQL session, the user must set a working graph. The USE GRAPH command sets or changes the user’s working graph, for the current session.

For more about roles and privileges, see the document

Managing User Privileges and Authentication v2.1

.


USE GRAPH syntax
USE GRAPH gname

Instead of the USE GRAPH command, gsql can be invoked with the -g <graph_name> option.


DROP GRAPH





Available to superusers only.

The effect of this command is modified, to take into account shared domains.


DROP GRAPH syntax
DROP GRAPH gname

The DROP GRAPH deletes the logical definition of the named graph. Futhermore, if any of the vertex types or edge types in its domain are not shared by any other graph, then those non-shared types and their data are deleted.  Any shared types are unaffected. To delete only selected vertex types or edge types, see DROP VERTEX | EDGE in the Section “Modifying a Graph Schema”.


Modifying a Graph Schema

After a graph schema has been created
, it can be modified. Data already stored in the graph and which is not logically part of the change will be retained. For example, if you had 100 Book vertices and then added an attribute to the Book schema, you would still have 100 Books, with default values for the new attribute. If you dropped a Book attribute, you still would have all your books, but one attribute would be gone.

To safely update the graph schema, the user should follow this procedure:

  • Create a SCHEMA_CHANGE JOB, which defines a sequence of ADD, ALTER and/or DROP statements.

  • Run the SCHEMA_CHANGE JOB (i.e.


    RUN JOB job_name


    ), which will do the following:

  • Attempt the schema change.

  • If the change is successful, invalidate any loading job or query definitions which are incompatible with the new schema.

  • if the change is unsuccessful, report the failure and return to the state before the attempt.

A schema change will invalidate any loading jobs or query jobs which relate to an altered part of the schema. Specifically:

  • A loading job becomes invalid if it refers to a vertex or and an edge which has been

    dropped

    (deleted) or

    altered

    .
  • A query becomes invalid if it refers to a vertex, and edge, or an attribute which has been

    dropped

    .

Invalid loading jobs are dropped, and invalid queries are uninstalled.

After the schema update, the user will need to c


reate and install new load and query jobs based on the new schema.


Jobs and queries for unaltered parts of the schema will still be available and do not need to be reinstalled.  However, even though these jobs are valid (e.g., they

can

be run), the user may wish to examine whether they still perform the preferred operations (e.g., do you

want

to run them?)


Load or query operations which begin before the schema change will be completed based on the pre-change schema. Load or query operations which begin after the schema change, and which have not been invalidated, will be completed based on the post-change schema.



Global vs. Local Schema Changes





Only admin, designer, and superuser users can create and run schema changes.  Each user role can create and run a different type of schema change.


Only a superuser can add, alter, or drop global vertex types or global edge types, which are those that are created using CREATE VERTEX or CREATE … EDGE.  This rule applies even if the vertex or edge type is used in only one graph. To make these changes, the superuser uses a GLOBAL SCHEMA_CHANGE JOB.


An admin or designer user can add, alter, or drop local vertex types or local edge types which are created in the context of that graph. Local vertex and edge types are created using an ADD statement inside a SCHEMA_CHANGE JOB. To alter or drop any of these local types, the admin user uses a regular SCHEMA_CHANGE JOB.


The two types of schema change jobs are described below.


CREATE SCHEMA_CHANGE JOB

The CREATE SCHEMA_CHANGE JOB block defines a sequence of ADD, ALTER, and DROP statements for changing a particular graph. It does not perform the schema change.


CREATE SCHEMA_CHANGE JOB syntax
CREATE SCHEMA_CHANGE JOB job_name FOR GRAPH graph_name {
[sequence of DROP, ALTER, and ADD statements, each line ending with a semicolon]
}

 

One use of CREATE SCHEMA_CHANGE JOB is to define an additional vertex type and edge type to be the structure for a secondary index. For example, if you wanted to index the postalCode attribute of the User vertex, you could create a postalCode_idx (PRIMARY_ID id string, code string) vertex type and hasPostalCode (FROM User, TO postalCode_idx) edge type. Then create an index structure having one edge from each User to a postalCode_idx vertex.

 


By its nature, a SCHEMA_CHANGE JOB may contain multiple statements. If the job block is used in the interactive GSQL shell, then the BEGIN and END commands should be used to permit the SCHEMA_CHANGE JOB to be entered on several lines. if the job is stored in a command file to be read in batch mode, then BEGIN and END are not needed.

Remember to include a semicolon at the end of each DROP, ALTER, or ADD statement within the JOB block.

If a SCHEMA_CHANGE JOB defines a new edge type which connects to a new vertex type, the ADD VERTEX statement should precede the related ADD EDGE statement. However, the ADD EDGE and ADD VERTEX statements can be in the same SCHEMA_CHANGE JOB.


ADD VERTEX | EDGE (local)

The ADD statement defines a new type of vertex or edge and automatically adds it to a graph schema. The syntax for the ADD VERTEX | EDGE statement is analogous to that of the CREATE VERTEX | EDGE | GRAPH statements.  It may only be used within a SCHEMA_CHANGE JOB.


ADD VERTEX / UNDIRECTED EDGE / DIRECTED EDGE
ADD VERTEX v_type_name (PRIMARY_ID id type [, attribute_list]) [WITH STATS=”none”|”outdegree_by_edgetype”];
ADD UNDIRECTED EDGE e_type_name (FROM v_type_name, TO v_type_name [, edge_attribute_list]);
ADD DIRECTED EDGE e_type_name (FROM v_type_name, TO v_type_name [, edge_attribute_list])
[WITH REVERSE_EDGE=”rev_name”];


ALTER VERTEX | EDGE

The ALTER statement is used to add attributes to or remove attributes from an existing vertex type or edge type. It may only be used within a SCHEMA_CHANGE JOB.  The basic format is as follows:


ALTER VERTEX / EDGE
ALTER VERTEX|EDGE object_type_name ADD|DROP (attribute_list);

ALTER … ADD

Added attributes are appended to the end of the schema.  The new attributes may include DEFAULT fields:


ALTER … ADD
ALTER VERTEX|EDGE object_type_name ADD ATTRIBUTE (
attribute_name type [DEFAULT default_value]
[, attribute_name type [DEFAULT default_value]]* );

ALTER … DROP


ALTER … DROP
ALTER VERTEX|EDGE object_type_name DROP ATTRIBUTE (
attribute_name [, attribute_name]* );

DROP VERTEX | EDGE (local)

The DROP statement removes the specified
vertex type or edge type
from the database dictionary. The DROP statement should only be used when graph operations are not in progress.


drop vertex / edge
DROP VERTEX v_type_name [, v_type_name]*
DROP EDGE e_type_name [, e_type_name]*


RUN SCHEMA_CHANGE JOB


RUN JOB job_name

performs the schema change job. After the schema has been changed, the GSQL system checks all existing GSQL queries (described in “GSQL Language Reference, Part 2: Querying”). If an existing GSQL query uses a dropped vertex, edge, or attribute, the query becomes invalid, and GSQL will show the message “Query

query_name

becomes invalid after schema update, please update it.”.

Below is an example. The schema ch
ange job add_reviews
adds a Review vertex type and two edge types to connect reviews to users and books, respectively.


SCHEMA_CHANGE JOB example
USE GRAPH Book_rating
CREATE SCHEMA_CHANGE JOB add_reviews FOR GRAPH Book_rating {
ADD VERTEX Review (PRIMARY_ID id UINT, review_date DATETIME, url STRING);
ADD UNDIRECTED EDGE wrote_review (FROM User, TO Review);
ADD UNDIRECTED EDGE review_of_book (FROM Review, TO Book);
}
RUN JOB add_reviews


USE GLOBAL






The USE GLOBAL command changes a superuser’s mode to Global mode.  In global mode, a superuser can define or modify global vertex and edge types, as well as specifying which graphs use those global types.  For example, the user should run USE GLOBAL before creating or running a GLOBAL SCHEMA_CHANGE JOB.


CREATE GLOBAL SCHEMA_CHANGE JOB






The CREATE GLOBAL SCHEMA_CHANGE JOB block defines a sequence of ADD, ALTER, and DROP statements which modify either the attributes or the graph membership of global vertex or edge types.  Unlike the non-global schema_change job, the header does not include a graph name. However, the ADD/ALTER/DROP statements in the body do mention graphs.


CREATE GLOBAL SCHEMA_CHANGE JOB syntax
CREATE GLOBAL SCHEMA_CHANGE JOB job_name {
[sequence of global DROP, ALTER, and ADD statements, each line ending with a semicolon]
}

Those both global and local schema change jobs have ADD and DROP statements, they have different meanings. The table below outlines the differences.

local

SCHEMA_CHANGE

GLOBAL

SCHEMA_CHANGE

ADD Defines a new local vertex/edge type;

adds it to the graph’s domain

Adds one or more existing global

vertex/edge types to a graph’s domain.

DROP Deletes a local vertex/edge type

and its vertex/edge instances

Removes one or more existing global

vertex/edge types from a graph’s domain.

ALTER Adds or drops attributes from a local

vertex/edge type.

Adds or drops attributes from a global vertex/edge

type, which may affect several graphs.

 


Remember to include a semicolon at the end of each DROP, ALTER, or ADD statement within the JOB block.


ADD VERTEX | EDGE (global)






The ADD statement adds existing global vertex or edge types to one of the graphs.


ADD VERTEX / UNDIRECTED EDGE / DIRECTED EDGE (Global)
ADD VERTEX v_type_name [,v_type_name…] TO gname;
ADD EDGE e_type_name [,e_type_name…] TO gname;


ALTER VERTEX | EDGE






The ALTER statement is used to add attributes to or remove attributes from an existing global vertex type or edge type. The ALTER VERTEX / EDGE syntax for global schema changes is the same as that for local schema change jobs.


ALTER VERTEX / EDGE
ALTER VERTEX|EDGE object_type_name ADD|DROP (attribute_list);

ALTER … ADD

Added attributes are appended to the end of the schema.  The new attributes may include DEFAULT fields:


ALTER … ADD
ALTER VERTEX|EDGE object_type_name ADD ATTRIBUTE (
attribute_name type [DEFAULT default_value]
[, attribute_name type [DEFAULT default_value]]* );

ALTER … DROP


ALTER … DROP
ALTER VERTEX|EDGE object_type_name DROP ATTRIBUTE (
attribute_name [, attribute_name]* );


DROP VERTEX | EDGE (global)






The DROP statement removes specified global vertex or edge types from one of the graphs. The command does not delete any data.


drop vertex / edge
DROP VERTEX v_type_name [,v_type_name…] FROM gname;
DROP EDGE e_type_name [,e_type_name…] FROM gname;


RUN GLOBAL SCHEMA_CHANGE JOB







RUN JOB job_name

performs the global schema change job. After the schema has been changed, the GSQL system checks all existing GSQL queries (described in “GSQL Language Reference, Part 2: Querying”). If an existing GSQL query uses a dropped vertex, edge, or attribute, the query becomes invalid, and GSQL will show the message “Query

query_name

becomes invalid after schema update, please update it.”.

Below is an example. The schema change alter_friendship_make_library drops the on_date attribute from the friend_of edge and adds Book type to the library graph.


GLOBAL SCHEMA_CHANGE JOB example
USE GLOBAL
CREATE GRAPH library()
CREATE GLOBAL SCHEMA_CHANGE JOB alter_friendship_make_library {
ALTER EDGE friend_of DROP ATTRIBUTE (on_date);
ADD VERTEX Book TO GRAPH library;
}
RUN JOB alter_friendship_make_library


Creating a Loading Job

Afte
r a graph sch
ema has been created, the system is ready to load data into the graph store. The GSQL language offers easy-to-understand and easy-to-use commands for data loading which perform many of the same data conversion, mapping, filtering, and merging operations which are found in enterprise ETL (Extract,Transform, and Load) systems.

The GSQL system can read structured or semistructured data from text files.  The loading language syntax is geared towards tabular or JSON data, but conditional clauses and data manipulation functions allow for reading data that is structured in a more complex or irregular way.  For tabular data, each line in the data file contains a series of data values, separated by commas, tabs, spaces, or any other designated ASCII characters (only single character separators are supported). A line should contain only data values and separators, without extra whitespace. From a tabular view, each line of data is a row, and each row consists of a series of column values.

Loading data is a two-step process. First, a loading job is defined.  Next, the job is executed with the RUN statement. These two statements, and the components with the loading job, are detailed below.

The structure of a loading job will be presented hierarchically, top-down:

CREATE … JOB, which may contain a set of DEFINE and LOAD statements

  • DEFINE statements
  • LOAD statements, which can have several clauses

New LOADING JOB Capabilities

Beginning with v2.0, the TigerGraph platform introduces an extended syntax for defining and running loading jobs which offers several advantages:

  • The TigerGraph platform can handle concurrent loading jobs, which can greatly increase throughput.
  • The data file locations can be specified at compile time or at run time. Run-time settings override compile-time settings.
  • A loading job definition can include several input files. When running the job, the user can choose to run only part of the job by specifying only some of the input files.
  • Loading jobs can be monitored, aborted, and restarted.

Concurrent Loading

Among its several duties, the RESTPP component manages loading jobs. Previously, RESTPP could manage only one loading job at a time. In v2.0, there can be multiple RESTPP-LOADER subcomponents, each of which can handle a loading job independently.  The maximum number of concurrent loading jobs is set by the configuration parameter RESTPP-LOADER.Replicas.

Furthermore, if the TigerGraph graph is distributed (partitioned) across multiple machine nodes, each machine’s RESTPP-LOADER(s) can be put into action. Each RESTPP-LOADER only reads local input data files, but the resulting graph data can be stored on any machine in the cluster.


To maximize loading performance in a cluster, use at least two loaders per machine, and assign each loader approximately the same amount of data.

To provide this added capability for loading, there is an expanded syntax for creating loading jobs and running loading jobs. Below is a summary of changes and additions. Full details are then presented, in the remainder of this document (GSQL Language Reference Part 1).

  • A loading job begins with CREATE LOADING JOB. (Note that the keyword “LOADING” is included.)
  • A new statement type, DEFINE FILENAME, is added, to define filename variables.
  • The file locations can refer either to the local machine, to specific machines, or to all machines.
  • When a job starts, it is assigned a job_id. Using the job_id, you can check status, abort a job, or restart a job.

Below is a simple example:


Concurrent Loading Job Example

CREATE LOADING JOB job1 FOR GRAPH graph1 {

DEFINE FILENAME file1 = “/data/v1.csv”;
DEFINE FILENAME file2;

LOAD file1 TO VERTEX v1 VALUES ($0, $1, $2);
LOAD file2 TO EDGE e2 VALUES ($0, $1);
}
RUN LOADING JOB job1 USING file1=”m1:/data/v1_1.csv”, file2=”m2:/data/e2.csv”

A concurrent-capable loading job can logically be separated into parts according to each file variable.  When a concurrent-capable loading job is compiled, a RESTPP endpoint is generated for each file variable. Then, the job can be run in portions, according to each file variable.

pre-v2.0 CREATE JOB syntax is deprecated


If the new CREATE LOADING JOB syntax with DEFINE FILENAME is used, the user can take advantage of concurrent loading.

Pre-v2.0 loading syntax will still be supported for v2.x but is deprecated. Pre-v2.0 loading syntax does not offer concurrent loading.

 


Example loading jobs and data files for the book_rating schema defined earlier in the document are available in the

/doc/examples/gsql_ref

folder in your TigerGraph platform installation.

 

CREATE LOADING JOB Block

The v2.0 CREATE LOADING JOB can be distinguished from the pre-v2.0 loading jobs first by its header, and then by whether its contains DEFINE FILENAME statements or not. Once the loading type has been determined, there are subsequent rules for the format of the individual LOAD statements and then the RUN statement.

Loading type Block Header Has

DEFINE FILENAME

statements?

Run
v2.0 loading CREATE LOADING JOB Yes RUN LOADING JOB
Non-concurrent offline loading

(DEPRECATED)

CREATE LOADING JOB No RUN JOB
Non-concurrent online loading

(DEPRECATED)

CREATE ONLINE_POST JOB Not permitted RUN JOB USING FILENAME…

The CREATE LOADING JOB and DROP LOADING JOB privileges are reserved for the designer, admin, and superuser roles.


CREATE LOADING JOB

The CREATE LOADING JOB statement is used to define a block of DEFINE, LOAD, and DELETE statements for loading data to or removing data from a particular graph. The sequence of statements is enclosed in curly braces. Each statement in the block, including the last one, should end with a semicolon.


CREATE LOAD for offline loading
CREATE LOADING JOB job_name FOR GRAPH graph_name {
[zero or more DEFINE statements, each ending in a semicolon]
[zero or more LOAD statements, each ending in a semicolon]
[zero or more DELETE statements, each ending in a semicolon]
}


DROP JOB statement

To drop (remove) a job, run “DROP JOB job_name”. The job will be removed from GSQL. To drop all jobs, run either of the following commands:

DROP JOB ALL

DROP JOB *


Scope of ALL




The scope of ALL depends on the user’s current scope. If the user has set a working graph, then DROP ALL removes all the jobs for that graph. If a superuser has set their scope to be global, then DROP ALL removes all jobs across all graph spaces.

 

DEFINE statements

A DEFINE statement is used to define a local variable or expression to be used by the subsequent LOAD statements in the loading job.

DEFINE FILENAME

The DEFILE FILENAME statement defines a filename variable.  The variable can then be used later in the JOB block by a LOAD statement to identify its data source. Every concurrent loading job must have at least one DEFINE FILENAME statement.


DEFINE FILENAME

filevar

["="

filepath_string

];



filepath_string

= (

path

|

"

all

:"

path


|


"

any

:"

path


|


mach_aliases


"

:"


path


[","

mach_aliases

":"

path

]* )



mach_aliases

= name["|"name]*

 

The

filevar

is optionally followed by a

filepath_string

, which tells the job where to find input data.  As the name suggests,

filepath_string

is a string value. Therefore, it should start and end with double quotes.


filepath_string

There are four options for

filepath_string

:



  1. path

    :

    either an absolute path or relative path for either a file or a folder on the machine where the job is run. If it is a folder, then the loader will attempt to load each non-hidden file in the folder.


    path examples
    “/data/graph.csv”

    If this path is not valid when CREATE LOADING JOB is executed, GSQL will report an error.

    An absolute path may begin with the session variable

    $sys.data_root.


    Example: using sys.data_root in a loading job
    CREATE LOADING JOB filePathEx FOR GRAPH gsql_demo {
    LOAD “$sys.data_root/persons.csv” TO
    }

    Then, when running this loading job, first set a value for the parameter, and then run the job:


    Example: Setting sys.data_root session parameter
    SET sys.data_root=”/data/mydata”
    RUN JOB filePathEx

    As the name implies, session parameters only retain their value for the duration of the current GSQL session.  If the user exits GSQL, the settings are lost.



  2. "all:"


    path


    : If the path is prefixed with

    all:

    , then the loading job will attempt to run on every machine in the cluster which has a RESTPP component, and each machine will look locally for data at

    path

    . I

    f the path is not valid on any of the machines, the job will be aborted

    .  Also, the session parameter


    $sys.data_root may not be used.

     


    ALL:path examples
    ALL:/data/graph.csv”



  3. "any:"

    path



    :

    If the path is prefixed with

    any:

    , then the loading job will attempt to run on every machine in the cluster which has a RESTPP component, and each machine will look locally for data at

    path

    .

    If the path is not valid on any of the machines, those machines are skipped.

    Also, the session parameter


    $sys.data_root may not be used.

     


    ANY:path examples
    ANY:/data/graph.csv”

  4. A list of machine-specific paths

    : A machine_alias is a name such as m1, m2, etc. which is defined when the cluster configuration is set.  For this option, the

    filepath_string

    may include a list of paths, separated by commas. If several machines have the same path, the paths can be grouped together by using a list of machine aliases, with the vertical bar “|” as a separator. The loading job will run on whichever machines are named; each RESTPP-LOADER will work on its local files.


    machine-specific path example
    “m1:/data1.csv, m2|m3|m5:/data/data2.csv”

DEFINE HEADER

The DEFINE HEADER statement defines a sequence of column names for an input data file. The first column name maps to the first column, the second column name maps to the second column, etc.


DEFINE HEADER

header_name

= "

column_name

"[,"

column_name

"]*;

DEFINE INPUT_LINE_FILTER

The DEFINE INPUT_LINE_FILTER statement defines a named Boolean expression whose value depends on column attributes from a row of input data. When combined with a USING reject_line_rule clause in a LOAD statement, the filter determines whether an input line is ignored or not.


DEFINE INPUT_LINE_FILTER

filter_name

=

boolean_expression_using_column_variables

;


LOAD statements

A LOAD statement tells the GSQL loader how to parse a data line into column values (tokens), and then describes how the values should be used to create a new vertex or edge instance. One LOAD statement can be used to generate multiple vertices or edges, each vertex or edge having its own

Destination_Clause

, as shown below. Additionally, two or more LOAD statements may refer to the same input data file. In this case, the GSQL loader will merge their operations so that both of their operations are executed in a single pass through the data file.


The LOAD statement has many options. This reference guide provides examples of key features and options. The


Platform Knowledge Base / FAQs


and the tutorials, such as


Get Started with TigerGraph


, provide additional solution- and application-oriented examples.



Different LOAD statement types have different rules for the USING clause; see the

USING clause

section below for specifics.

LOAD statement


LOAD [filepath_string|filevar|TEMP_TABLE

table_name

]

Destination_Clause

[,

Destination_Clause

]* [USING

clause

];

The

filevar

must have been previously defined in a DEFINE FILENAME statement.

The

filepath_string

must satisfy the same rules given above in the DEFINE FILENAME section.

“__GSQL_FILENAME_n__”: Position-based File Identifiers


When a CREATE LOADING JOB block is processed, the GSQL system will count the number of unique filepath_strings and assign them position-based index numbers 0, 1, 2, etc. starting from the top. A filepath_string is considered one item, even if it has multiple machine indexes and file locations. These index numbers can then be used as an alternate naming scheme for the filespath_strings:

When running a loading job, the nth filepath_string can be referred as “__GSQL_FILENAME_n__”, where n is replaced with the index number.  Note that the string has double underscores at both the left and right ends.

 

The remainder of this section of the document will provide details on the format and use of the file_path, Destination_Clause, its subclauses. USING clause is introduced later in Section “Other Optional LOAD Clauses”.

Destination Clause

A

Destination_Clause

describes how the tokens from a data source should be used to construct one of three types of

data objects

: a vertex, an edge, or a row in a temporary table (TEMP_TABLE). The destination clause formats for the three types are very similar, but we show them separately for clarity:


Vertex Destination Clause
TO VERTEX vertex_type_name VALUES (id_expr [, attr_expr]*)
[WHERE conditions] [OPTION (options)]

 


Edge Destination Clause
TO EDGE edge_type_name VALUES (source_id_expr, target_id_expr [, attr_expr]*)
[WHERE conditions] [OPTION (options)]

 


TEMP_TABLE Destination Clause
TO TEMP_TABLE table_name (id_name [, attr_name]*) VALUES (id_expr [, attr_expr]*)
[WHERE conditions] [OPTION (options)]

For the TO VERTEX and TO EDGE destination clauses, the

vertex_type_name

or

edge_type_name

must match the name of a vertex or edge type previously defined in a CREATE VERTEX or CREATE UNDIRECTED|DIRECTED EDGE statement.  The values in the VALUE list

(id_expr, attr_expr1, attr_expr2,…)

are assigned to the id(s) and attributes of a new vertex or edge instance, in the same order in which they are listed in the CREATE statement.

id_expr

obeys the same attribute rules as

attr_expr

, except that

only attr_expr

can use the reducer function, which is introduced later.

In contrast, the TO TEMP_TABLE clause is defining a new, temporary data structure.  Its unique characteristics will be described in a separate subsection. For now, we focus on TO VERTEX and TO EDGE.


Attributes and Attribute Expressions

A LOAD statement processes each line of an input file, splitting each line (according to the SEPARATOR character, see Section “Other Optional LOAD Clauses” for more details) into a sequence of tokens. Each destination clause provides a token-to-attribute mapping which defines how to construct a new vertex, an edge, or a temp table row instance (e.g., one data object). The tokens can also be thought of as the column values in a table. There are two ways to refer to a column, by position or by name.  Assuming a column has a name, either method may be used, and both methods may be used within one expression.


By Position

: The columns (tokens) are numbered from left to right, starting with $0.  The next column is $1, and so on.


By Name

: Columns can be named, either through a header line in the input file, or through a DEFINE HEADER statement.  If a header line is used, then the first line of the input file should be structured like a data line, using the same separator characters, except that each column contains a column name string instead of a data value. Names are enclosed in double quotes, e.g. $”age”.


Data file name:

$sys.file_name refers to the current input data file.

In a simple case, a token value is copied directly to an attribute. For example, in the following LOAD statement,


Example: using $sys.file_name in an attribute expression
LOAD “xx/yy/a.csv” TO VERTEX person VALUES ($0, $1, $sys.file_name)
  • The PRIMARY_ID of a person vertex comes from column $0 of the file “xx/yy/a.csv”.
  • The next attribute of a person vertex comes from column $1.
  • The next attribute of a person vertex is given the value “xx/y/a.csv” (the filename itself).

Cumulative Loading

A basic principle in the GSQL Loader is cumulative loading. Cumulative loading means that a particular data object might be written to (i.e., loaded) multiple times, and the result of the multiple loads may depend on the full sequence of writes. This usually means that If a data line provides a valid data object, and the WHERE clause and OPTION clause are satisfied, then the data object is loaded.


  1. Valid input

    : For each input data line, each destination clause constructs one or more new data objects. To be a

    valid


    data object,

    it must have an ID value of the correct type, have correctly typed attribute values, and satisfy the optional WHERE clause. If the data object is not valid, the object is rejected (skipped) and counted as an error in the log file. The rules for invalid attributes values are summarized below:

    1. UINT: Any non-digit character. (Out-of-range values cause overflow instead of rejection)
    2. INT: Any non-digit or non-sign character.
      (Out-of-range values cause overflow instead of rejection)
    3. FLOAT and DOUBLE: Any wrong format
    4. STRING, STRING COMPRESS, FIXED_BINARY: N/A
    5. DATETIME: Wrong format, invalid date time, or out of range.
    6. Complex type: Depends on the field type or element type. Any invalid field (in UDT), element (in LIST or SET), key or value (in MAP) causes rejection.

  2. New data objects:

    If a valid data object has a new ID value, then the data object is added to the graph store.  Any attributes which are missing are assigned the default value for that data type or for that attribute.

  3. Overwriting existing data objects

    : If a valid data object has a ID value for an existing object, then the new object overwrites the existing data object, with the following clarifications and exceptions:

    1. The attribute values of the new object overwrite the attribute values of the existing data object.

    2. Missing tokens

      : If a token is missing from the input line so that the generated attribute is missing, then that attribute retains its previous value.


      A STRING token is never considered missing; if there are no characters, then the string is the empty string


  4. Skipping an attribute

    : A LOAD statement can specify that a particular attribute should NOT be loaded by using the special character _ (underscore) as its attribute expression (attr_expr).  For example,

    LOAD TO VERTEX person VALUES ($0, $1, _, $2)

    means to skip the next-to-last attribute.  This technique is used when it is known that the input data file does not contain data for every attribute.

    1. If the LOAD is creating a new vertex or edge, then the skipped attribute will be assigned the default value.
    2. If the LOAD is overwriting an existing vertex or edge, then the skipped attribute will retain its existing value.


More Complex Attribute Expressions

An attribute expression may use column tokens (e.g., $0), literals (constant numeric or string values), any of the built-in loader token functions, or a user-defined token function. Attribute expressions may

not

contain mathematical or boolean operators (such as +, *, AND). The rules for attribute expressions are the same as those for id expressions, but an attribute expression can additionally use a reducer function:


  • id_expr

    := $column_number | $”column_name” | constant | $sys.file_name | token_function_name(

    id_expr

    [,

    id_expr

    ]* )

  • attr_expr

    :=

    id_expr

    | REDUCE(reducer_function_name(id

    _expr

    ))

Note that token functions can be nested, that is, a token function can be used as an input parameter for another token function. The built-in loader token/reducer functions and user-defined token functions are described in the section “Built-In Loader Token Functions”.

The subsections below describe details about loading particular data types.

Loading a DOUBLE or FLOAT Attribute

A floating point value has the basic format


[sign][digits].digits[[sign](e|E)digits]

or

[sign]digits[.[digits]][[sign](e|E)digits]

 

In the first case, the decimal point and following digits are required. In the second case, some digits are required (looking like an integer), and the following decimal point and digits are optional.

In both cases, the leading sign ( “+” or “-“) is optional. The exponent, using “e” or “E”, is optional. Commas and extra spaces are not
allowed.


Examples of valid and invalid floating point values
# Valid floating point values
-198256.03
+16.
-.00036
7.14285e15
9.99E-22# Invalid floating point values
-198,256.03
9.99 E-22

 

Loading a DATETIME Attribute

When loading data into a DATETIME attribute, the GSQL loader will automatically read a string representation of datetime information and convert it to internal datetime representation.  The loader accepts any of the following string formats:


  • %Y-%m-%d %H:%M:%S

    (e.g., 2011-02-03 01:02:03)

  • %Y/%m/%d %H:%M:%S

    (e.g., 2011/02/03 01:02:03)

  • %Y-%m-%dT%H:%M:%S.000z

    (e.g., 2011-02-03T01:02:03.123z, 123 will be ignored)

  • %Y-%m-%d

    (only date, no time, e.g., 2011-02-03 )

  • %Y/%m/%d

    (only date, no time, e.g., 2011/02/03)
  • Any integer value (Unix Epoch time, where Jan 1, 1970 at 00:00:00 is integer 0)

Format notation:

%Y is a 4-digit year. A 2-digit year is not a valid value.

%m and %s are a month (1 to 12) and a day (1 to 31), respectively.  Leading zeroes are optional.

%H, %M, %S are hours (0 to 23), minutes (0 to 59) and seconds (0 to 59), respectively. Leading zeroes are optional.

When loading data, the loader checks whether the values of year, month, day, hour, minute, second are out of the valid range. If any invalid value is present, e.g. ‘2010-13-05’ or ‘2004-04-31 00:00:00’, the attribute is invalid and the object (vertex or edge) is not created.

Loading a User-Defined Type (UDT) Attribute

To load a UDT attribute, state the name of the UDT type, followed by the list of attribute expressions for the UDT’s fields, in parentheses. See the example below.


Load UDT example
TYPEDEF TUPLE <f1 INT (1), f2 UINT, f3 STRING (10), f4 DOUBLE > myTuple # define a UDT
CREATE VERTEX v_udt (PRIMARY_ID id STRING, att_udt myTuple)
CREATE LOADING JOB load_udt FOR GRAPH test_graph {
DEFILE FILENAME f;
LOAD f TO VERTEX v_udt VALUES ($0, myTuple($1, $2, $3, $4) ); # $1 is loaded as f1, $2 is loaded as f2, and so on
}
RUN LOADING JOB v_udt USING f=”./udt.csv”

Loading a LIST or SET Attribute

There are three methods to load a LIST or a SET.

The first method is to load multiple rows of data which share the same id values and append the individual attribute values to form a collection of values. The collections are formed incrementally by reading one value from each eligible data line and appending the new value into the collection. When the loading job processes a line, it checks to see whether a vertex or edge with that id value(s) already exists or not. If the id value(s) is new, then a new vertex or edge is created with a new list/set containing the single value. If the id(s) has been used before, then the value from the new line is appended to the existing list/set. Below shows an example:


Example: Cumulative loading of multiple rows to a SET/LIST
CREATE VERTEX test_vertex (PRIMARY_ID id STRING, iset SET<INT>, ilist LIST<INT>)
CREATE UNDIRECTED EDGE test_edge(FROM test_vertex, TO test_vertex)
CREATE GRAPH test_set_list (*)CREATE LOADING JOB load_set_list FOR GRAPH test_set_list {
DEFINE FILENAME f;
LOAD f TO VERTEX test_vertex VALUES ($0, $1, $1);
}
RUN LOADING JOB load_set_list USING f=”./list_set_vertex.csv”


list_set_vertex.csv


list_set_vertex.csv
1,10
3,30
1,20
3,30
3,40
1,20

The job load_set_list  will load two test_vertex vertices because there are two unique id values in the data file. Vertex 1 has attribute values with iset = [10,20] and ilist = [10,20,20]. Vertex 3 has values iset = [30,40] and ilist = [30, 30, 40]. Note that a set doesn’t contain duplicate values, while a list can contain duplicate values.


Because GSQL loading is multi-threaded, the order of values loaded into a LIST might not match the input order.

If the input file contains multiple columns which should be all added to the LIST or SET, then a second method is available. Use the LIST() or SET() function as in the example below:


Example: loading multiple columns to a SET/LIST
CREATE VERTEX v_set (PRIMARY_ID id STRING, nick_names SET<STRING>)
CREATE VERTEX v_list (PRIMARY_ID id STRING, lucky_nums LIST<INT>)
CREATE GRAPH test_graph (*)
CREATE LOADING JOB load_set_list FOR GRAPH test_graph {
DEFINE FILENAME f;
LOAD f TO VERTEX v_set VALUES ($0, SET($1,$2,$3) );
LOAD f TO VERTEX v_list VALUES ($0, LIST($2,$4) );
}

The third method is to use the

SPLIT

() function to read a compound token and split it into a collection of elements, to form a LIST or SET collection. The SPLIT() function takes two arguments: the column index and the element separator. The element separator should be distinct from the separator through the whole file. Below shows an example:


Example: SET/LIST loading by SPLIT() example
CREATE VERTEX test_vertex (PRIMARY_ID id STRING, ustrset SET<STRING>, ilist LIST<INT>)
CREATE UNDIRECTED EDGE test_edge(FROM test_vertex, TO test_vertex)
CREATE GRAPH test_split (*)CREATE LOADING JOB set_list_job FOR GRAPH test_split {
DEFINE FILENAME f;
LOAD f TO VERTEX test_vertex VALUES ($0, SPLIT($1,”|”) , SPLIT($2,”#”) );
}
RUN LOADING JOB set_list_job USING f=”./split_list_set.csv”


split_list_set.csv


split_list_set.csv


vid,names,numbers


v1,mike|tom|jack,


1


#


2


#


3


v2,john,


5


#


4


#


8

 


The SPLIT() function cannot be used for UDT type elements.

Loading a MAP Attribute

There are three methods to load a MAP.

The first method is to load multiple rows of data which share the same id values. The maps are formed incrementally by reading one key-value pair from each eligible data line. When the loading job processes a line, it checks to see whether a vertex or edge with that id value(s) already exists or not. If the id value(s) is new, then a new vertex or edge is created with a new map containing the single key-value pair. If the id(s) has been used before, then the loading job checks whether the key exists in the map or not. If the key doesn’t exist in the map, the new key-value pair is inserted. Otherwise, the value will be replaced by the new value.


The loading order might not be the same as the order in the raw data. If a data file contains multiple lines with the same id and same key but different values, loading them together results in a nondeterministic final value for that key.


Method 1

: Below is the syntax to load a MAP by the first method: Use an arrow (->)  to separate the map’s key and value.


Loading a MAP by method 1: -> separator
CREATE VERTEX v_map (PRIMARY_ID id STRING, att_map MAP<INT, STRING>)
CREATE GRAPH test_graph (*)
CREATE LOADING JOB load_map FOR GRAPH test_graph {
DEFINE FILENAME f;
LOAD f TO VERTEX v_map VALUES ($0, ($1 -> $2) );
}

 


Method 2


: The second method is to use the MAP() function. If there are multiple key-value pairs among multiple columns, MAP() can load them together. Below is an example:


Loading a MAP by method 2: MAP() function
CREATE VERTEX v_map (PRIMARY_ID id STRING, att_map MAP<INT, STRING>)
CREATE GRAPH test_graph (*)
CREATE LOADING JOB load_map FOR GRAPH test_graph {
DEFINE FILENAME f;
LOAD f TO VERTEX v_map VALUES ($0, MAP( ($1 -> $2), ($3 -> $4) ) ); # $1 and $3 are keys and $2 and $4 are the corresponding values.
}

 


Method 3

: The third method is to use the SPLIT() function. Similar to the SPLIT() in loading LIST or SET, the SPLIT() function can be used when the key-value pair is in one column and separated by a key-value separator, or multiple key-value pairs are in one column and separated by element separators and key-value separators. SPLIT() here has three parameters: The first is the column index, the second is the key-value separator, and the third is the element separator. The third parameter is optional. If one row of raw data only has one key-value pair, the third parameter can be skipped. Below are the examples without and with the given element separator.


one_key_value.csv


example data with one key-value pair per line
vid,key_value
v1,1:mike
v2,2:tom
v1,3:lucy


multi_key_value.csv


example data with multiple key-value pairs per line
vid,key_value_list
v1,1:mike#4:lin
v2,2:tom
v1,3:lucy#1:john#6:jack

 


Loading a MAP by method 3: SPLIT() function
CREATE VERTEX v_map (PRIMARY_ID id STRING, att_map MAP<INT, STRING>)
CREATE GRAPH test_graph (*)
CREATE LOADING JOB load_map FOR GRAPH test_graph {
DEFINE FILENAME f;
LOAD f TO VERTEX v_map VALUES ($0, SPLIT($1, “:”, “#”) );
}

The SPLIT() function cannot be used for UDT type elements.

Loading Wildcard Type Edges

If an edge has been defined using a wildcard vertex type, a vertex type name must be specified, following the vertex id, in a load statement for the edge. An example is shown below:


Example: explicit vertex typing for an untyped edge
#schema setup
CREATE VERTEX user(PRIMARY_ID id UINT)
CREATE VERTEX product(PRIMARY_ID id UINT)
CREATE VERTEX picture(PRIMARY_ID id UINT)
CREATE UNDIRECTED EDGE purchase (FROM *, TO *)
CREATE GRAPH test_graph(*)#loading job
CREATE LOADING JOB test2 FOR GRAPH test_graph {
DEFINE FILENAME f;
LOAD f
TO EDGE purchase VALUES ($0 user, $1 product),
TO EDGE purchase VALUES ($0 user, $2 picture);
}

Built-in Loader Token Functions

The GSQL Loader provides several built-in functions which operate on tokens. Some may be used to construct attribute expressions and some may be used for conditional expressions in the WHERE clause.

Token Functions for Attribute Expressions

The following token functions can be used in an id or attribute expression

Function name and parameters Output type Description of function
gsql_reverse(

main_string

)
string Returns a string with the characters in the reverse order of the input string

main_string

.
gsql_concat(

string1, string2,…,stringN

)
string Returns a string which is the concatenation of all the input strings.
gsql_split_by_space(

main_string

)
string Returns a modified version of

main_string

, in which each space character is replaced with ASCII 30 (decimal).
gsql_to_bool(

main_string

)
bool Returns true if the

main_string

is either “t” or “true”, with case insensitive checking. Returns false otherwise.
gsql_to_uint(

main_string

)
uint If

main_string

is the string representation of an unsigned int, the function returns that integer.If

main_string

is the string representation of a nonnegative float, the function returns that number cast as an int.
gsql_to_int(

main_string

)
int If

main_string

is the string representation of an int, the function returns that integer.If

main_string

is the string representation of a float, the function returns that number cast as an int.
gsql_ts_to_epoch_seconds(

main_string

)
uint Converts a timestamp in canonical string format to Unix epoch time, which is the int number of seconds since Jan. 1, 1970. The

main_string

should be in one of the following 3 formats:
"%Y-%m-%d %H:%M:%S"

“%Y/%m/%d %H:%M:%S”

“%Y-%m-%dT%H:%M:%S.000z”

// text after . is ignored

gsql_current_time_epoch(0) uint Returns the current time in Unix epoch seconds. *By convention, the input parameter should be 0, but it is ignored.
flatten(

column_to_be_split, group_separator, 1

)flatten(

column_to_be_split, group_separator, sub_field_separator, number_of_sub_fields_in_one_group

)
See the section “TEMP_TABLE and Flatten Functions” below.

 


flatten_json_array (

$”array_name”

)

flatten_json_array (

$”array_name”, $”sub_obj_1″, $”sub_obj_2″, …, $”sub_obj_n”

)

 

See the section “TEMP_TABLE and Flatten Functions” below.
split(

column_to_be_split, element_separator

)split(

column_to_be_split, key_value_separator, element
_separator

)
See the section “Loading a LIST or SET Attribute” above.

See the section “Loading a MAP Attribute” above.

Reducer Functions

A reducer function aggregates multiple values of a non-id attribute into one attribute value of a single vertex or edge. Reducer functions are computed incrementally; that is, each time a new input token is applied, a new resulting value is computed.

To reduce and load aggregate data to an attribute, the attribute expression has the form



REDUCE(

reducer_function

(

input_expr

) )

where

reducer_function

is one of the functions in the table below.

input_expr

can include non-reducer functions, but reducer functions cannot be nested.

Each

reducer function is overloaded so that one function can be used for several different data types. For primitive data types, the output type is the same as the

input_expr

type. For LIST, SET, and MAP containers, the

input_expr

type is one of the allowed element types for these containers (see “Complex Types” in the Attribute Data Types section).  The output is the entire container.

Function name Data type of

arg

: Description of function’s return value
max(

arg

)
INT, UINT, FLOAT, DOUBLE: maximum of all

arg

values cumulatively received
min(

arg

)
INT, UINT, FLOAT, DOUBLE: minimum of all

arg
values

cumulatively

received

add(

arg

)
INT, UINT, FLOAT, DOUBLE: sum of all

arg

valuescumulatively

received

STRING: concatenation ofall arg values

cumulatively

received

LIST, SET element: list/set of all

arg

values

cumulatively

received

MAP (key -> value) pair: key-value dictionary of all key-value pair

arg

values

cumulatively

received

and(

arg

)
BOOL: AND of all


arg


values cumulatively receivedINT, UINT: bitwise AND of all


arg


values cumulatively received
or(

arg

)
BOOL: OR of all

arg
values cumulatively received

INT, UINT: bitwise OR of all

arg

values cumulatively received

overwrite(

arg

)
non-container:

arg
LIST, SET: new list/set containing only

arg
ignore_if_exists(

arg

)
Any: If an attribute value already exists, return(retain) the existing value. Otherwise, return(load)

arg

.

 


Each function supports a certain set of attribute types.


Calling a reducer function with an incompatible type crashes the service. In order to prevent that, use the WHERE clause (introduced below) together with IS NUMERIC or other operators, functions, predicates for type checking if necessary.



WHERE Clause

The WHERE clause is an optional clause. The WHERE clause’s condition is a boolean expression.  The expression may use column token variables, token functions, and operators which are described below. The expression is evaluated for each input data line. If the condition is true, then the vertex or edge instance is loaded into the graph store. If the condition is false, then this instance is skipped. Note that all attribute values are treated as string values in the expression, so the type conversion functions to_int() and to_float(), which are described below, are provided to enable numerical conditions.

Operators in the WHERE Clause

The GSQL Loader language supports most of the standard arithmetic, relational, and boolean operators found in C++.

Standard operator precedence applies, and parentheses provide the usual override of precedence.




  • Arithmetic Operators: +, -, *, /, ^
     



    Numeric operation can be used to express complex operation between numeric types. Just as in ordinary mathematical expressions, parentheses can be used to define a group and to modify the order of precedence.
     






    Because computers necessarily can only store approximations for most DOUBLE and FLOAT type values, it is not recommended to perform test for exact equality or inequality.  Instead, o
    ne should allow
    for an acceptable amount of error. The following example checks if $0 = 5, with an error of 0.00001 permitted:

    WHERE to_float($0) BETWEEN 5-0.00001 AND 5+0.00001

     

     



  • Relational Operators: <, >, ==, !=, <=, >=
     



    Comparisons can be performed between two numeric values or between two string values.



  • Predicate Operators:



    • AND, OR, NOT

      operators are the same as in SQL. They can be used to combine multiple conditions together.
      E.g.,

      $0 < “abc” AND $1 > “abc”

      selects the rows with the first token less than “abc” and the second token greater than “abc”.

      E.g.,

      NOT $1 < “abc”

      selects the rows with the second token greater than or equal to “abc”.



    • IS NUMERIC
      token


      IS NUMERIC

      returns true if

      token

      is in numeric format. Numeric format include integers, decimal notation, and exponential notation. Specifically, IS NUMERIC is true if token matches the following regular expression: (+/-)

      ?

      [0-9]

      +

      (.[0-9])

      ?

      [0-9]

      *

      ((e/E)(+/-)

      ?

      [0-9]

      +

      )

      ?

      . Any leading space and trailing space is skipped, but no other spaces are allowed.
      E.g.,

      $0 IS NUMERIC

      checks whether the first token is in numeric format.



    • IS EMPTY
      token


      IS EMPTY

      returns true if

      token

      is an empty string.
      E.g.,

      $1 IS EMPTY

      checks whether the second token is empty.



    • IN

      token

      IN



      (

      set_of_values

      )

      returns true if

      token

      is equal to one member of a set of specified values. The values may be string or numeric types.
      E.g.,

      $2 IN (“abc”, “def”, “lhm”)

      tests whether the third token equals one of the three strings in the given set.

      E.g.,

      to_int($3) IN (10, 1, 12, 13, 19)

      tests whether the fourth token equals one of the specified five numbers.



    • BETWEEN … AND


      token





      BETWEEN

      lowerVal

      AND

      upperVal


      returns true if

      token

      is within the specified range, inclusive of the endpoints. The values may be string or numeric types.
      E.g.,

      $4 BETWEEN “abc” AND “def”

      checks whether the fifth token is greater than or equal to “abc” and also less than or equal to “def”

      E.g.,

      to_float($5) BETWEEN 1 AND 100.5

      checks whether the sixth token is greater than or equal to 1.0 and less than or equal to 100.5.

Token functions in the WHERE clause

The GSQL loading language provides several built-in functions for the WHERE clause.

Function name Output type Description of function
to_int(

main_string

)
int Converts

main_string

to an integer value.
to_float(

main_string

)
float Converts

main_string

to a float value.
concat(

string1, string2

)
string Returns a string which is the concatenation of

string1

and

string2

.
token_len(

main_string

)
int Returns the length of

main_string.
gsql_is_not_empty_string(

main_string

)
bool Returns true if

main_string

is empty after removing white space. Returns false otherwise.
gsql_token_equal(

string1, string2

)
bool Returns true if

string1

is exactly the same (case sensitive) as

string2

. Returns false otherwise.
gsql_token_ignore_case_equal(

string1, string2

)
bool Returns true if

string1

is exactly the same (case insensitive) as

string2

. Returns false otherwise.
gsql_is_true(

main_string

)
bool Returns true if

main_string

is either “t” or “true” (case insensitive). Returns false otherwise.
gsql_is_false(

main_string

)
bool Returns true if

main_string

is either “f” or “false” (case insensitive). Returns false otherwise.

 


The token functions in the WHERE clause and those token functions used for attribute expression are different. They cannot be used exchangeably.


User-Defined Token Functions

Users can write their own token functions in C++ and install them in the GSQL system. The system installation already contains a source code file containing sample functions. Users simply add their customized token functions to this file.  The file for user-defined token functions for attribute expressions or WHERE clauses is at <tigergraph.root.dir>/dev/gdk/gsql/src/TokenBank/TokenBank.cpp. There are a few examples in this file, and details are presented below
.

 

Testing your functions is simple. In the same directory with the TokenBank.cpp file is a command script called compile.

 

  1. To test that your function compiles:

    ./compile
  2. To test that your function works correctly, write your own test and add it to the main() procedure in the TokenBank.cpp.  Then, compile the file and run it. Note that files located in ../TokenLib need to be included:

    g++ -I../TokenLib TokenBank.cpp
    ./a.out

User-defined Token Functions
for Attribute Expressions

Attribute type Function signature

string or string compress

extern “C” void funcName (const char* const iToken[], uint32_t iTokenLen[], uint32_t iTokenNum,
char* const oToken, uint32_t& oTokenLen)

bool
 

extern “C” bool funcName (const char* const iToken[], uint32_t iTokenLen[], uint32_t iTokenNum)
 
uint
extern “C”


uint64_t


funcName (const char* const iToken[], uint32_t iTokenLen[], uint32_t iTokenNum)


int
extern “C”


int64_t


funcName (const char* const iToken[], uint32_t iTokenLen[], uint32_t iTokenNum)


float
extern “C”


float


funcName (const char* const iToken[], uint32_t iTokenLen[], uint32_t iTokenNum)


double
extern “C”


double


funcName (const char* const iToken[], uint32_t iTokenLen[], uint32_t iTokenNum)


The parameters are as follows: iToken is the array of string tokens, iTokenLen is the array of the length of the string tokens, and iTokenNum is the number of tokens. Note that the input tokens are always in string (char*) format.

If the attribute type is not string nor string compress, the return type should be the corresponding type: bool for bool; uint64_t for uint; int64_t for int; float for float double for double. If the attribute type is string or string compress, the return type should be void, and use the extra parameters (

char *const oToken, uint32_t& oTokenLen) for storing the return string. oToken is the returned string value, and oTokenLen is the length of this string.

 

The

built-in token function gsql_concat is used as an example below. It takes multiple-token parameter and returns a string.


gsql_concat
extern “C” void gsql_concat(const char* const iToken[], uint32_t iTokenLen[], uint32_t iTokenNum, char* const oToken, uint32_t& oTokenLen) {
int k = 0;
for (int i=0; i < iTokenNum; i++) {
for (int j =0; j < iTokenLen[i]; j++) {
oToken[k++] = iToken[i][j];
}
}
oTokenLen = k;
}

User-defined Token Functions for WHERE Clause

User-defined token functions (described above) can also be used to construct the boolean conditional expression in the WHERE clause. However, there are some restrictions in the WHERE clause:


In the clause “WHERE

conditions

“,

  • The only type of user-defined token function allowed are those that return a boolean value.
  • If a user-defined token function is used in a WHERE Clause, then it must constitute the entire condition; it cannot be combined with another function or operator to produce a subsequent value. However, the arguments of the UDF can include other functions.

 


The source code for the built-in token function gsql_token_equal is used as an example for how to write a user-defined token function.


gsql_token_equal
extern “C” bool gsql_token_equal(const char* const iToken[], uint32_t iTokenLen[], uint32_t iTokenNum) {
if (iTokenNum != 2) {
return false;
}
if (iTokenLen[0] != iTokenLen[1]) {
return false;
}
for (int i =0; i < iTokenLen[0]; i++) {
if (iToken[0][i] != iToken[1][i]) {
return false;
}
}
return true;
}


Other Optional LOAD Clauses


OPTION clause

There are no supported options for the OPTION clause at this time.


USING clause


A USING clause contains one or more parameter value pairs:

USING parameter=value [,parameter=value]*

 


In the v2.0 loading syntax, the USING clause only appears at the end of a LOAD statement.

In earlier versions, the location of the USING clause and which parameters were valid depending the whether the job was a v1.x online loading job or v1.x offline loading job.

If multiple LOAD statements use the same source (the same file path, the same TEMP_TABLE, or the same file variable), the USING clauses in these LOAD statements must be the same. Therefore, we recommend that if multiple destination clauses share the same source, put all of these destination clauses into the same LOAD statement.

 


The following USING parameters are supported. The abbreviations have the following meanings:



“–” = “Not applicable”, “Opt” = “optional”, “REQ” = “required”.


Parameter
v2.0 LOAD

Stmt


Meaning of Value

Allowed Values
SEPARATOR Opt specifies the special character that separates tokens (columns) in the data file any single ASCII character.

Default is comma “,”


"\t"

for tab


"\xy"

for ASCII decimal code

xy

EOL Opt the end-of-line character any ASCII sequence

Default =


"\n"


(system-defined newline character or character sequence)

QUOTE

(See note below)

Opt specifies explicit boundary markers for string tokens, either single or double quotation marks. See more details below. “single” for ‘

“double” for “

USER_DEFINED_HEADER Opt specifies the name of the header variable, when a header has been defined in the loading job, rather than in the data file the variable name in the preceding DEFINE HEADER statement
REJECT_LINE_RULE Opt if the filter expression evaluates to true, then do not use this input data line. name of filter from a preceding DEFINE INPUT_LINE_FILTER statement
JSON_FILE

(See note below)

Opt whether each line is a json object (see Section “JSON Loader” below for more details) “true”, “false”

Default is “false”

HEADER Opt whether the data file’s first line is a header line.

The header assigns names to the columns.

The LOAD statement must refer to an actual file with a valid header.

“true”, “false”

Default is “false”

 


QUOTE parameter

The parser will not treat separator characters found within a pair of quotation marks as a separator. For example, if the parsing conditions are QUOTE=”double”, SEPARATOR=”,”, the comma in “Leonard,Euler” will not separate Leonard and Euler into separate tokens.

 

  • If QUOTE is not declared, quotation marks are treated as ordinary characters.
  • If QUOTE is declared, but a string does not contain a matching pair of quotation marks, then the string is treated as if QUOTE is not declared.
  • Only the string inside the first pair of quote (from left to right) marks are loaded. For example QUOTE=”double”, the string a”b”c”d”e will be loaded as b.
  • There is no escape character in the loader, so the only way to include quotation marks within a string is for the string body to use one type of quote (single or double) and to declare the other type as the string boundary marker.


Loading JSON Data

When the USING option JSON_FILE=”true” is used, the loader loads JSON objects instead of tabular data. A JSON object is an unordered set of key/value pairs, where each value may itself be an array or object, leading to nested structures.  A colon separates each key from its value, and a comma separates items in a collection.  A more complete description of JSON format is available at

www.json.org

. The JSON loader requires that

each input line has exactly one JSON object

. Instead of using column values as tokens, the JSON loader uses JSON values as tokens, that is, the second part of each JSON key/value pair. In a GSQL loading job, a JSON field is identified by a dollar sign $ followed by the colon-separated sequence of nested key names to reach the value from the top level. For example, given the JSON object {“abc”:{“def”: “this_value”}}, the identifier $”abc”:”def” is used to access “this_value”. T

he double quotes are mandatory.


An example is shown below:


USING JSON_FILE test schema and loading job
CREATE VERTEX encoding (PRIMARY_ID id STRING, length FLOAT default 10)
CREATE UNDIRECTED EDGE encoding_edge (FROM encoding, TO encoding)
CREATE GRAPH encoding_graph (*)CREATE LOADING JOB json_load FOR GRAPH encoding_graph {
LOAD “encoding.json” TO VERTEX encoding
VALUES ($”encoding”, $”indent”:”length”) USING JSON_FILE=”true”;
}
RUN JOB json_load


encoding.json


encoding.json
{“encoding”: “UTF-7″,”plug-ins”:[“c”],”indent” : { “length” : 30, “use_space”: true }}
{“encoding”:”UTF-1″,”indent”:{“use_space”: “dontloadme”}, “plug-ins” : [null, true, false] }
{“plug-ins”:[“C”,”c++”],”indent”:{“length” : 3, “use_space”: false},”encoding”:”UTF-6″}


In the above data encoding.json, the order of fields are not fixed and some fields are missing. The JSON loader ignores the order and accesses the fields by the nested key names. The missing fields are loaded with default values. The result vertices are:

id attr1
“UTF-7” 30
“UTF-1” 10
“UTF-6” 3


TEMP_TABLE and Flatten Functions

The keyword TEMP_TABLE triggers the use of a temporary data table which is used to store data generated by one LOAD statement, for use by a later LOAD statement. Earlier we introduced the syntax for loading data to a TEMP_TABLE:


TEMP_TABLE Destination Clause
TO TEMP_TABLE table_name (id_name [, attr_name]*) VALUES (id_expr [, attr_expr]*)
[WHERE conditions] [OPTION (options)]

This clause is designed to be used in conjunction with the flatten or flatten_json_array function in one of the attr_expr expressions. The flatten function splits a multi-value field into a set of records. Those records can first be stored into a temporary table, and then the temporary table can be loaded into vertices and/or edges. Only one flatten function is allowed in one temp table destination clause.

There are two versions of the flatten function: One parses single-level groups and the other parses two-level groups. There are also two versions of the flatten_json_array function: One splits an array of primitive values, and the other splits an array of JSON objects.

One-Level Flatten Function

flatten(

column_to_be_split, separator, 1

) is used to parse a one-level group into individual elements. An example is shown below:


book1.dat


book1.dat
101|”Harry Potter and the Philosopher’s Stone”|”fiction,fantasy,young adult”
102|”The Three-Body Problem”|”fiction,science fiction,Chinese”

One-level Flatten Function loading (load_book_flatten1.gsql)
CREATE LOADING JOB load_books_flatten1 FOR GRAPH Book_rating {
DEFINE FILENAME f;
LOAD f
TO VERTEX Book VALUES ($0, $1, _),
TO TEMP_TABLE t1(bookcode,genre) VALUES ($0, flatten($2,”,”,1))
USING QUOTE=”double”, SEPARATOR=”|”;LOAD TEMP_TABLE t1
TO VERTEX Genre VALUES($”genre”, $”genre”),
TO EDGE book_genre VALUES($”bookcode”, $”genre”);
}
RUN LOADING JOB load_books_flatten1 USING f=”../data/book1.dat”

The loading job contains two LOAD statements.  The first one loads input data to Book vertices and to a TEMP_TABLE. The second one loads the TEMP_TABLE data to Genre vertices and book_genre edges.

bookcode genre
101 fiction
101 fantasy
101 young_adult
102 fiction
102 science_fiction
102 Chinese

Line 5 says that the third column ($2) of each input line should be split into separate tokens, with comma “,” as the separator. Each token will have its own row in table t1. The first column is labeled “bookcode” with value $0 and the second column is “genre” with one of the $2 tokens.  The contents of TEMP_TABLE t1 are shown below:

Then, lines 8 to 10 say to read TEMP_TABLE t1 and to do the following for each row:

  • Create a Genre vertex for each new value of “genre”.
  • Create a book_genre edge from “bookcode” to “genre”.  In this case, each row of TEMP_TABLE t1 generates one book_genre edge.

The final graph will contain two Book vertices (101 and 102), five Genre vertices, and six book_genre edges.


List of all book_genre edges after loading
{
“results”: [{“@@edgeSet”: [
{
“from_type”: “Book”,
“to_type”: “Genre”,
“directed”: false,
“from_id”: “101”,
“to_id”: “fiction”,
“attributes”: {},
“e_type”: “book_genre”
},
{
“from_type”: “Book”,
“to_type”: “Genre”,
“directed”: false,
“from_id”: “101”,
“to_id”: “fantasy”,
“attributes”: {},
“e_type”: “book_genre”
},
{
“from_type”: “Book”,
“to_type”: “Genre”,
“directed”: false,
“from_id”: “102”,
“to_id”: “sciencevfiction”,
“attributes”: {},
“e_type”: “book_genre”
},
{
“from_type”: “Book”,
“to_type”: “Genre”,
“directed”: false,
“from_id”: “101”,
“to_id”: “young adult”,
“attributes”: {},
“e_type”: “book_genre”
},
{
“from_type”: “Book”,
“to_type”: “Genre”,
“directed”: false,
“from_id”: “102”,
“to_id”: “fiction”,
“attributes”: {},
“e_type”: “book_genre”
},
{
“from_type”: “Book”,
“to_type”: “Genre”,
“directed”: false,
“from_id”: “102”,
“to_id”: “Chinese”,
“attributes”: {},
“e_type”: “book_genre”
}
]}]
}

 

Two-Level Flatten Function

flatten(

column_to_be_split, group_separator, sub_field_separator, number_of_sub_fields_in_one_group

) is used for parse a two-level group into individual elements. Each token in the main group may itself be a group, so there are two separators: one for the top level and one for the second level. An example is shown below.


book2.dat
101|”Harry Potter and the Philosopher’s Stone”|”FIC:fiction,FTS:fantasy,YA:young adult”
102|”The Three-Body Problem”|”FIC:fiction,SF:science fiction,CHN:Chinese”

The flatten function now has four parameters instead of three.  The additional parameter is used to record the genre_name in the Genre vertices.


Two-level Flatten Function loading (book_flatten2_load.gsql)
CREATE LOADING JOB load_books_flatten2 FOR GRAPH Book_rating {
DEFINE FILENAME f;
LOAD f
TO VERTEX Book VALUES ($0, $1, _),
TO TEMP_TABLE t2(bookcode,genre_id,genre_name) VALUES ($0, flatten($2,”,”,”:”,2))
USING QUOTE=”double”, SEPARATOR=”|”;LOAD TEMP_TABLE t2
TO VERTEX Genre VALUES($”genre_id”, $”genre_name”),
TO EDGE book_genre VALUES($”bookcode”, $”genre_id”);
}
RUN LOADING JOB load_books_flatten2 USING f=”book2.dat”

In this example, in the genres column ($2), there are multiple groups, and each group has two sub-fields, genre_id and genre_name. After running the loading job, the file book2.dat will be loaded into the TEMP_TABLE t2 as shown below.

bookcode genre_id
genre_name
101 FIC fiction
101 FTS fantasy
101 YA young adult
102 FIC fiction
102 SF science fiction
102 CHN Chinese

Flatten a JSON Array of Primitive Values

flatten_json_array($”

array_name

“) parses a JSON array of primitive (string, numberic, or bool) values, where “array_name” is the name of the array. Each value in the array creates a record. Below is an example:


flatten_json_array_values loading
CREATE VERTEX encoding (PRIMARY_ID id STRING, length FLOAT default 10)
CREATE UNDIRECTED EDGE encoding_edge (FROM encoding, TO encoding)
CREATE GRAPH encoding_graph (*)CREATE LOADING JOB json_flatten FOR GRAPH encoding_graph {
LOAD “encoding2.json” TO TEMP_TABLE t2 (name, length)
VALUES (flatten_json_array($”plug-ins”), $”indent”:”length”) USING JSON_FILE =”true”;
LOAD TEMP_TABLE t2
TO VERTEX encoding VALUES ($”name”, $”length”);
}
RUN LOADING JOB json_flatten


encoding2.json


encoding2.json
{“plug-ins” : [“C”, “c++”],”encoding” : “UTF-6″,”indent” : { “length” : 3, “use_space”: false}}

 

The above data and loading job creates the following temporary table:

id length
C 3
c++ 3

Flatten a JSON Array of JSON Objects

flatten_json_array (

$”array_name”, $”sub_obj_1″, $”sub_obj_2″, …, $”sub_obj_n”

) parses a JSON array of JSON objects. “array_name” is the name of the array, and the following parameters

$”sub_obj_1″, $”sub_obj_2″, …, $”sub_obj_n”

are the field key names in each object in the array. See complete example below:


encoding3.json


encoding3.json
{“encoding”:”UTF-1″,”indent”:{“use_space”: “dontloadme”}, “plug-ins” : [null, true, false, {“lang”:”golang”,”prop”:{“age”:”noidea”}}]}
{“encoding”: “UTF-8”, “plug-ins” : [{“lang”: “pascal”, “score”:”1.0″, “prop”:{“age”:”old”}}, {“lang”:”c++”, “score”:2.0}],”indent”:{“length” :12,”use_space”: true}}
{“encoding”: “UTF-7”, “plug-ins” : [{“lang”:”java”, “score”:2.22}, {“lang”:”python”, “score”:3.0},{“lang”:”go”, “score”:4.0, “prop”:{“age”:”new”}}],”indent” : { “length” : 30, “use_space”: true }}
{“plug-ins” : [“C”, “c++”],”encoding” : “UTF-6″,”indent” : { “length” : 3, “use_space”: false}}

 


json_flatten_array_test.gsql
CREATE VERTEX encoding3 (PRIMARY_ID id STRING, score FLOAT default -1.0, age STRING default “Unknown”, length INT default -1)
CREATE UNDIRECTED EDGE encoding3_edge (FROM encoding3, TO encoding3)
CREATE GRAPH encoding_graph (*)CREATE LOADING JOB json_flatten_array FOR GRAPH encoding_graph {
LOAD “encoding3.json” TO TEMP_TABLE t3 (name, score, prop_age, indent_length )
VALUES (flatten_json_array($”plug-ins”, $”lang”, $”score”, $”prop”:”age”), $”indent”:”length”)
USING JSON_FILE=”true”;
LOAD TEMP_TABLE t3
TO VERTEX encoding3 VALUES ($”name”, $”score”, $”prop_age”, $”indent_length”);
}
RUN LOADING JOB json_flatten_array

When splitting a JSON array of JSON objects, the primitive values are skipped and only JSON objects are processed. As in the example above, the 4th line’s “plug-ins” field will not generate any record because its “plug-ins” array doesn’t contain any JSON object. Any field which does not exist in the object will be loaded with default value. The above example generates the temporary table shown
below:

id score age length
“golang”
default
“noidea”
default
“pascal” 1.0 “old” 12
“c++” 2.0

default
 
12
“java” 2.22
default
30
“python” 3.0
default
30
“go” 4.0 “new” 30

Flatten a JSON Array in a CSV file


flatten_json_array()

can also be used to split a column of a tabular file, where the column contains JSON arrays. An example is given below:


encoding.csv


encoding.csv
golang|{“prop”:{“age”:”noidea”}}
pascal|{“score”:”1.0″, “prop”:{“age”:”old”}}
c++|{“score”:2.0, “indent”:{“length”:12, “use_space”: true}}
java|{“score”:2.22, “prop”:{“age”:”new”}, “indent”:{“use_space”:”true”, “length”:2}}
python|{ “prop”:{“compiled”:”false”}, “indent”:{“length”:4}, “score”:3.0}
go|{“score”:4.0, “prop”:{“age”:”new”}}

 

The second column in the csv file is a JSON array which we want to split. flatten_json_array() can be used in this case without  the USING JSON_FILE=”true” clause:


json_flatten_cvs.gsql
CREATE VERTEX encoding3 (PRIMARY_ID id STRING, score FLOAT default -1.0, age STRING default “Unknown”, length INT default -1)
CREATE UNDIRECTED EDGE encoding3_edge (FROM encoding3, TO encoding3)
CREATE GRAPH encoding_graph (*)CREATE LOADING JOB json_flatten_cvs FOR GRAPH encoding_graph {
LOAD “encoding.csv” TO TEMP_TABLE t4 (name, score, prop_age, indent_length )
VALUES ($0,flatten_json_array($1, $”score”, $”prop”:”age”, $”indent”:”length”))
USING SEPARATOR=”|”;
LOAD TEMP_TABLE t4
TO VERTEX encoding3 VALUES ($”name”, $”score”, $”prop_age”, $”indent_length”);
}
RUN LOADING JOB json_flatten_cvs

The above example generates the temporary table shown below:

id score age length
golang -1 (default) noidea -1 (default)
pascal 1 old -1 (default)
c++ 2 unknown (default) 12
java 2.22 new 2
python 3 unknown (default) 4
go 4 new -1 (default)

 

flatten_json_array in csv



flatten_json_array() does not work if the separator appears also within the json array column. For example, if the separator is comma, the csv loader will erroneously divide the json array into multiple columns. Therefore, it is recommended that the csv file use a special column separator, such as “|” in the above example .


DELETE statement

In addition to loading data, a LOADING JOB can be used to perform the opposite operation: deleting vertices and edges, using the DELETE statement. DELETE cannot be used in offline loading. Just as a LOAD statement uses the tokens from each input line to set the id and attribute values of a vertex or edge to be created, a DELETE statement uses the tokens from each input line to specify the id value of the item(s) to be deleted.


In the v2.0 syntax, there is now a ”

FROM (filepath_string | filevar)

” clause just before the WHERE clause.

 

There are four variations of the DELETE statement. The syntax of the four cases is shown below.


DELETE VERTEX | EDGE Syntax
CREATE LOADING JOB abc FOR GRAPH graph_name {
DEFINE FILENAME f;
# 1. Delete each vertex which has the given vertex type and primary id.
DELETE VERTEX vertex_type_name (PRIMARY_ID id_expr) FROM f [WHERE condition] ;# 2. Delete each edge which has the given edge type, source vertex id, and destination vertex id.
DELETE EDGE edge_type_name (FROM id_expr, TO id_expr) FROM f [WHERE condition] ;# 3. Delete all edges which have the given edge type and source vertex id. (Destination vertex id is left open.)
DELETE EDGE edge_type_name (FROM id_expr) FROM f [WHERE condition] ;

# 4. Delete all edges which have the given source vertex id. (Edge type and destination vertex id are left open.)
DELETE EDGE * (FROM id_expr vertex_type_name) FROM f [WHERE condition] ;
}

An example using book_rating data is shown below:


DELETE example
# Delete all user occupation edges if the user is in the new files, then load the new files
CREATE LOADING JOB clean_user_occupation FOR GRAPH Book_rating {
DEFINE FILENAME f;
DELETE EDGE user_occupation (FROM $0) FROM f;
}
CREATE LOADING JOB load_user_occupation FOR GRAPH Book_rating {
DEFINE FILENAME f;
LOAD f TO EDGE user_occupation VALUES ($0,$1);
}
RUN LOADING JOB clean_user_occupation USING f=”./data/user_occupation_update.dat”
RUN LOADING JOB load_user_occupation USING f=”./data/user_occupation_update.dat”

There is a separate DELETE statement in the GSQL Query Language. The query delete statement can leverage the query language’s ability to explore the graph and to use complex conditions to determine which items to delete. In contrast, the loading job delete statement requires that the id values of the items to be deleted must be specified in advance in an input file.


offline2online Job Conversion (DEPRECATED)

offline2online <offline_job_name>

The gsql command offline2online converts an installed offline loading job to an equivalent online loading job or set of jobs.

Online Job Names

An offline loading job contains one or more LOAD statements, each one specifying the name of an input data file.  The offline2online will convert each LOAD statement into a separate online loading job. The data filename will be appended to the offline job name, to create the new online job name.  For example, if the offline job has this format:

CREATE LOADING JOB loadEx FOR GRAPH graphEx {
LOAD “fileA” TO
LOAD “fileB” TO
}

then running the GSQL command


offline2online loadEx


will create two new online loading jobs, called

loadEx_fileA

and

loadEx_fileB

. The converted loading jobs are installed in the GSQL system; they are not available as text files. However, if there are already jobs with these names, then a version number will be appended: first “_1”, then “_2”, etc.

For example, if you were to execute


offline2online loadEx


three times, this would generate the following online jobs:

  • 1st time:  loadEx_fileA, loadEx_fileB
  • 2nd time: loadEx_fileA_1, loadEx_fileB_1
  • 3rd time:  loadEx_fileA_2, loadEx_fileB_2

Conversion and RUN JOB Details


Some parameters of a loading job which are built in to offline loading jobs instead cannot be included in online jobs:

  • input data filename
  • SEPARATOR
  • HEADER

Instead, they should be provided when running the loading job. However, online jobs do not have full support for HEADER.

When running any online loading job, the input data filename and the separator character must be provided.  See sections on the


USING clause


and


Running a Loading Job


for more details.

If an online loading job is run with the HEADER=”true” option, it will skip the first line in the data file, but it will not read that line to get the column names.  Therefore, offline jobs which read and use column header names must be manually converted to online jobs.

The following example is taken from the Social Network case in the

GSQL Tutorial with Real-Life Examples

. In version 0.2 of the tutorial, we used offline loading. The job below uses the same syntax as v0.2, but some names have been updated:


Offline loading example, based on social_load.gsql, version 0.2
CREATE LOADING JOB load_social FOR GRAPH gsql_demo
{
LOAD “data/social_users.csv”
TO VERTEX SocialUser VALUES ($0,$1,$2,$3)
USING QUOTE=”double”, SEPARATOR=”,”, HEADER=”true”;LOAD “data/social_connection.csv”
TO EDGE SocialConn VALUES ($0, $1)
USING SEPARATOR=”,”, HEADER=”false”;
}

To run, this job:

RUN LOADING JOB load_social

Note that the first LOAD statement has HEADER=”true”, but is does not make use of column names. It simply uses column indices $0, $1, $2, and $3. Therefore, the HEADER option can still be used with the converted job. Running

offline2online load_social1

, creates two new jobs called

load_social_social_users.csv

and

load_social_social_connection.csv.

The equivalent run commands for the jobs are the following:

RUN LOADING JOB load_social_social_users.csv USING FILENAME=”data/social_users.csv”, SEPARATOR=”,”, EOL=”\n”, HEADER=”true”
RUN LOADING JOB load_social_social_connection.csv USING FILENAME=”data/social_connection.csv”, SEPARATOR=”,”, EOL=”\n”

 

For comparison, here is the online loading job in the current version of the Tutorial and its loading commands:


social_load.gsql, version 0.8.1
CREATE LOADING JOB load_social1 FOR GRAPH gsql_demo
{
LOAD
TO VERTEX SocialUser VALUES ($0,$1,$2,$3) USING QUOTE=”double”;
}
CREATE LOADING JOB load_social2 FOR GRAPH gsql_demo {
LOAD
TO EDGE SocialConn VALUES ($0, $1);
}
# load the data
RUN JOB load_social1 USING FILENAME=”../social/data/social_users.csv”, SEPARATOR=”,”, EOL=”\n”, HEADER=”true”
RUN JOB load_social2 USING FILENAME=”../social/data/social_connection.csv”, SEPARATOR=”,”, EOL=”\n”


Running a Loading Job

Clearing and Initializing the Graph Store

There are two aspects to clearing the system: flushing the data and clearing the schema definitions in the catalog. Two different commands are available.

CLEAR GRAPH STORE




Available only to superusers.

The CLEAR GRAPH STORE command flushes all the data out of the graph store (database).  By default, the system will ask the user to confirm that you really want to discard all the graph data.  To force the clear operation and bypass the confirmation question, use the -HARD option, e.g.,



CLEAR GRAPH STORE -HARD

Clearing the graph store
does not affect the schema.


  1. Use the -HARD option with extreme caution. There is no undo option. -HARD must be in all capital letters.
  2. CLEAR GRAPH STORE stops all the TigerGraph servers (GPE, GSE, RESTPP, Kafka, and Zookeeper).
  3. Loading jobs and queries are aborted.

DROP ALL




Available only to superusers.

The DROP ALL statement clears the graph store and removes all definitions from the catalog: vertex types, edge types, graph types, jobs, and queries.

 

Running a Loading Job

Running a loading job executes a previously installed loading job.  The job reads lines from an input source, parses each line into data tokens, and applies loading rules and conditions to create new vertex and edge instances to store in the graph data store.

TigerGraph 2.0 introduces enhanced data loading with slightly modified syntax for CREATE  and RUN statements.  The previous RUN JOB syntaxes for v1.x online loading and offline loading and still supported for backward compatibility. Additionally, loading jobs can also be run by directly submitted a HTTP request to the REST++ server.

RUN LOADING JOB

pre-v2.0 RUN JOB syntax is deprecated


As of v2.0, RUN LOADING JOB is the preferred syntax for running all loading jobs.  The pre-v2.0 syntaxes for running online post jobs and offline loading jobs are still support for now but are deprecated.


RUN LOADING JOB syntax for concurrent loading
RUN LOADING JOB [-noprint] [-dryrun] [-n [i],j] jobname [ USING filevar [=”filepath_string”][, filevar [=”filepath_string”]]* [, CONCURRENCY=”cnum”][,BATCH_SIZE=”bnum”]]

Note that the keyword LOADING is included. This makes it more clear to users and to GSQL that the job is a loading job and not some other type of job ( such as a SCHEMA_CHANGE JOB).

When a concurrent loading job is submitted, it is assigned a job ID number, which is displayed on the GSQL console.  The user can use this job ID to refer to the job, for a status update, to abort the job, or to re-start the job.  These operations are described later in this section.


Options



-noprint

By default, the command will print several several lines of status information while the loading is running.

If the -noprint option is included, the job will run omit the progress and summary details, but it will still display the job id and the location of the log file.


Example of minimal output when -noprint option is used
Kick off the following job:
JobName: load_videoE, jobid: gsql_demo_m1.1525091090494
Loading log: ‘/usr/local/tigergraph/logs/restpp/restpp_loader_logs/gsql_demo/gsql_demo_m1.1525091090494.log’



-dryrun

If -dryrun is used, the system will read the data files and process the data as instructed by the job, but will NOT load any data into the graph. This option can be a useful diagnostic tool.



-n [i], j

The


-n


option limits the loading job to processing only a range of lines of each input data file. The -n flag accepts one or two arguments. For example,


-n 50


means read lines 1 to 50.



-n 10, 50


means read lines 10 to 50.  The special symbol $ is interpreted as “last line”, so


-n 10,$


means reads from line 10 to the end.



filevar

list

The optional USING clause may contain a list of file variables. Each file variable may optionally be assigned a

filepath_string

, obeying the same format as in the CREATE LOADING JOB. This list of file variables determines which parts of a loading job are run and what data files are used.

  1. When a loading job is compiled, it generates one RESTPP endpoint for each

    filevar and filepath_string

    .  As a consequence, a loading job can be run in parts. When RUN LOADING JOB is executed, only those endpoints whose

    filevar

    or file identifier (”

    __GSQL_FILENAME_n__"

    ) is mentioned in the USING clause will be used. However, if the USING clause is omitted, then the entire loading job will be run.
  2. If a

    filepath_string

    is given, it overrides the

    filepath_string

    defined in the loading job.
    If a particular

    filevar

    is not assigned a

    filepath_string

    either in the loading job or in the RUN LOADING JOB statement, then an error is reported and the job exits.



CONCURRENCY

The CONCURRENCY parameter sets the maximum number of concurrent requests that the loading job may send to the GPE.  The default is 256.



BATCH_SIZE

The BATCH_SIZE parameter sets the number of data lines included in each concurrent request sent to the GPE.  The default is 1024.

Running Loading Jobs as REST Requests

Another way to run a loading job is to submit an HTTP request to the

POST /ddl/<graph_name>

endpoint of the REST++ server. Since the REST++ server has more direct access to the graph processing engine, this can execute more quickly than a RUN LOADING JOB statement in GSQL.

When a CREATE LOADING JOB block is executed, the GSQL system creates one REST endpoint for each file source. Therefore, one REST request can invoke loading for one file source at a time. Running an entire loading job may take more than one REST request.

The Linux curl command is a handy way to make HTTP requests. If the data size is small, it can be included directly in the command line by using the -d flag with a data string:


Curl/REST++ syntax for loading using the POST /ddl endpoint
curl -X POST -d “<data_string>” “http://<server_ip>:9000/ddl/<graph_name>?<parameters>?tag=<job_name>&filename=<filepath><&optional_parameters>

If the data size is large, it is better to reference the data filename, using the –data-binary flag:


Curl/REST++ syntax for loading using the POST /ddl endpoint
curl -X POST –data-binary @<data_filename> “http://<server_ip>:9000/ddl/<graph_name>?tag=<job_name>&filename=<filename_variable><&optional_parameters>

<filepath> should be replaced with either a file variable (from a DEFINE FILENAME statement) or a position-based file identifier (“__GSQL_FILENAME_n__”) for an explicit filepath_string.

For more information, about sending REST++ requests, see the

RESTPP API User Guide

.


Example

: The code block below shows three equivalent commands for the same loading job.  The first uses the gsql command RUN JOB. The second uses the Linux curl command to support a HTTP request, placing the parameter values in the URL’s query string. T
he third gives
the parameter values through the curl command’s data
payload -d option.


REST++ ddl loading examples
# Case 1: Using GSQL
GSQL -g gsql_demo RUN LOADING JOB load_cf USING FILENAME=”../cf/data/cf_data.csv”# Case 2: Using REST++ Request with data in a file, where file1 is one of the filename variables defined in the loading job
curl -X POST –data-binary @data/cf_data.csv “http://localhost:9000/ddl/gsql_demo?&tag=load_cf&filename=file1&sep=,&eol=\n”# Case 3: Using REST++ Request with data inline, where file1 is one of the filename variables defined in the loading job
curl -X POST -d
“id2,id1\nid2,id3\nid3,id1\nid3,id4\nid5,id1\nid5,id2\nid5,id4” “http://localhost:9000/ddl/gsql_demo?tag=load_cf&filename=file1&sep=,&eol=\n”

 

Inspecting and Managing Loading Jobs

Starting with v2.0, there are now commands to checking loading job status, to abort a loading job and to restart a loading job.

Job ID and Status

When a loading job starts, the GSQL server assigns it a job id and displays it for the user to see. The job id format is typically the name of the loading job, followed by the machine alias, following by a code number, e.g.,

gsql_demo_m1.1525091090494


Example of SHOW LOADING STATUS output
Kick off the following job, i.e.
JobName: load_test1, jobid: poc_graph_m1.1523663024967
Loading log: ‘/home/tigergraph/tigergraph/logs/restpp/restpp_loader_logs/demo_graph/demo_graph_m1.1523663024967.log’Job “demo_graph_m1.1523663024967” loading status[RUNNING] m1 ( Finished: 3 / Total: 4 )
[LOADING] /data/output/company.data
[============= ] 20%, 200 kl/s
[LOADED]
+——————————————————————-+
| FILENAME | LOADED LINES | AVG SPEED | DURATION|
| /data/output/movie.dat | 100 | 100 l/s | 1.00 s|
|/data/output/person.dat | 100 | 100 l/s | 1.00 s|
| /data/output/roles.dat | 200 | 200 l/s | 1.00 s|
+——————————————————————-+
[RUNNING] m2 ( Finished: 1 / Total: 2 )
[LOADING] /data/output/company.data
[========================== ] 60%, 200 kl/s
[LOADED]
+——————————————————————-+
| FILENAME | LOADED LINES | AVG SPEED | DURATION|
| /data/output/movie.dat | 100 | 100 l/s | 1.00 s|
+——————————————————————-+

By default, an active loading job will display periodic updates of its progress.  There are two ways to inhibit these automatic output displays:

  1. Run the loading job with the -noprint option.
  2. After the loading job has started, enter CTRL+C. This will abort the output display process, but the loading job will continue.

SHOW LOADING STATUS

The command SHOW LOADING JOB shows the current status of either a specified loading job or all current jobs:


SHOW LOADING JOB syntax
SHOW LOADING STATUS job_id|ALL

The display format is the same as that displayed during the periodic progress updates of the RUN LOADING JOB command. If you do not know the job id, but you know the job name and possibly the machine, then the ALL option is a handy way to see a list of active job ids.

ABORT LOADING JOB

The command

ABORT LOADING JOB aborts either a specified load job or all active loading jobs:


ABORT LOADING JOB syntax
ABORT LOADING JOB job_id|ALL

The output will show a summary of aborted loading jobs.


ABORT LOADING JOB example

gsql -g demo_graph “abort loading job all”

Job “demo_graph_m1.1519111662589” loading status
[ABORT_SUCCESS] m1
[SUMMARY] Finished: 0 / Total: 2
+————————————————————————————–+
|                  FILENAME |   LOADED LINES |   AVG SPEED  |   DURATION |   PERCENTAGE|
| /home/tigergraph/data.csv |       23901701 |     174 kl/s |   136.83 s |         65 %|
|/home/tigergraph/data1.csv |              0 |        0 l/s |     0.00 s |          0 %|
+————————————————————————————–+

Job “demo_graph_m2.1519111662615” loading status
[ABORT_SUCCESS] m2
[SUMMARY] Finished: 0 / Total: 2
+————————————————————————————–+
|                  FILENAME |   LOADED LINES |   AVG  SPEED |   DURATION |   PERCENTAGE|
| /home/tigergraph/data.csv |       23860559 |     175 kl/s |   136.23 s |         65 %|
|/home/tigergraph/data1.csv |              0 |        0 l/s |     0.00 s |          0 %|
+————————————————————————————–+

RESUME LOADING JOB

The command

RESUME LOADING JOB will restart a previously-run job which ended for some reason before completion.


RESUME LOADING JOB syntax
RESUME LOADING JOB job_id

If the job is finished, this command will do nothing. The RESUME command should pick up where the previous run ended; that is, it should not load the same data twice.


RESUME LOADING JOB example
gsql -g demo_graph “RESUME LOADING JOB demo_graph_m1.1519111662589″
[RESUME_SUCCESS] m1
[MESSAGE] The current job got resummed

Verifying and Debugging a Loading Job

Every loading job creates a log file. When the job starts, it will display the location of the log file. Typically, the file is located at

<TigerGraph.root.dir>/logs/restpp/restpp_loader_logs/<graph_name>/<job_id>.log

This file contains the following information which most users will find useful:

  • A list of all the parameter and option settings for the loading job
  • A copy of the status information that is printed
  • Statistics report on the number of lines successfully read and parsed

The statistics report include how many objects of each type is created, and how many lines are invalid due to different reasons. This report also shows which lines cause the errors. Here is the list of statistics shown in the report. There are two types of statistics. One is file level (the number of lines), and the other is data object level (the number of objects). If an file level error occurs, e.g., a line does not have enough columns, this line of data is skipped for all LOAD statements in this loading job. If an object level error or failed condition occurs, only the corresponding object is not created, i.e., all other objects in the same loading job are still created if no object level error or failed condition for each corresponding object.

File level statistics Explanation
Valid lines
The number of valid lines in the source file
Reject lines
The number of lines which are rejected by reject_line_rules
Invalid Json format
The number of lines with invalid JSON format
Not enough token
The number of lines with missing column(s)
Oversize token
The number of lines with oversize token(s). Please increase “OutputTokenBufferSize” in the


tigergraph/dev/gdk/gsql/config file.

 

Object level statistics Explanation
Valid Object
The number of objects which have been loaded successfully
No ID found
The number of objects in which PRIMARY_ID is empty
Invalid Attributes
The number of invalid objects caused by wrong data format for the attribute type
Invalid primary id
The number of invalid objects caused by wrong data format for the PRIMARY_ID type
Incorrect fixed
binary length
The number of invalid objects caused by the mismatch of the length of the data to the type defined in the schema

Note that failing a WHERE clause is not necessarily a bad result.  If the user’s intent for the WHERE clause is to select only certain lines, then it is natural for some lines to pass and some lines to fail.

 

Below is an example.

CREATE VERTEX movie (PRIMARY_ID id UINT, title STRING, country STRING COMPRESS, year UINT)
CREATE DIRECTED EDGE sequel_of (FROM movie, TO movie)
CREATE GRAPH movie_graph(*)
CREATE LOADING JOB load_movie FOR GRAPH movie_graph{
DEFINE FILENAME f
LOAD f TO VERTEX movie VALUES ($0, $1, $2, $3) WHERE to_int($3) < 2000;
}
RUN LOADING JOB load_movie USING f=”movie.dat”

 


movie.dat
0,abc,USA,-1990
1,abc,CHN,1990
2,abc,CHN,1990
3,abc,FRA,2015
4,abc,FRA,2005
5,abc,USA,1990
6,abc,1990

The above loading job and data generate the following report


load_output.log (tail)
——————–Statistics——————————
Valid lines: 6
Reject lines: 0
Invalid Json format: 0
Not enough token: 1 [ERROR] (e.g. 7)
Oversize token: 0Vertex: movie
Valid Object: 3
No ID found: 0
Invalid Attributes: 1 [ERROR] (e.g. 1:year)
Invalid primary id: 0
Incorrect fixed
binary length: 0
Passed condition lines: 4
Failed condition lines: 2 (e.g. 4,5)

There are a total of 7 data lines. The report shows that

  • Six of the lines are valid data lines
  • One line (Line 7) does not have enough tokens.

Of the 6 valid lines,

  • Three of the 6 valid lines generate valid movie vertices.
  • One line has an invalid attribute  (Line 1: year)
  • Two lines (Lines 4 and 5) do not pass the WHERE clause.

 


Back to Top


Appendix A – DDL Keywords and Reserved Words

The following words are reserved for use by the Data Definition Language.  That is, a graph schema or loading job may not use any of these words for a user-defined identifier, for the name of a vertex type, edge type, graph, or attribute.

The compiler will reject the use of a Reserved Word as a user-defined identifier.

ABORT ACCESS ADD ADMIN
AFTER ALL ALLOCATE ALTER
ANALYZE AND ANY ARCHIVE
ARE ARRANGE ARRAY AS
ASC ASENSITIVE ASYMMETRIC AT
ATOMIC ATTRIBUTE AUTHORIZATION AV
AVG BAG BASIC BEFORE
BEGIN BETWEEN BIGINT BINARY
BINSTORAGE BLOB BOOL BOOLEAN
BOTH BUCKET BUCKETS BY
BYTEARRAY CACHE CALL CALLED
CASCADE CASCADED CASE CAST
CAT CD CHANGE CHAR
CHARACTER CHARARRAY CHECK CLEAR
CLOB CLOSE CLUSTER CLUSTERED
CLUSTERSTATUS COGROUP COLLATE COLLECTION
COLUMN COLUMNS COMMENT COMMIT
COMPACT COMPACTIONS COMPRESS COMPUTE
CONCAT CONCATENATE CONDITION CONF
CONNECT CONST CONSTRAINT CONTINUE
COPYFROMLOCAL COPYTOLOCAL CORRESPONDING COUNT
CP CREATE CROSS CUBE
CURRENT CURRENT_DATE CURRENT_PATH CURRENT_ROLE
CURRENT_TIME CURRENT_TIMESTAMP CURRENT_USER CURSOR
CYCLE DATA DATABASE DATABASES
DATE DATETIME DAY DBPROPERTIES
DD DEALLOCATE DEC DECIMAL
DECLARE DECRYPT DEFAULT DEFERRED
DEFINE DEFINED DELETE DELIMITED
DEPENDENCY DEREF DESC DESCRIBE
DETERMINISTIC DIFF DIRECTED DIRECTORIES
DIRECTORY DISABLE DISCONNECT DISTINCT
DISTRIBUTE DM DO DOUBLE
DROP DRYRUN DU DUMP
DYNAMIC EACH EDGE ELEMENT
ELEM_TYPE ELSE ELSEIF EMPTY
ENABLE END EOL ESCAPE
ESCAPED EVAL EXCEPT EXCHANGE
EXCLUSIVE EXEC EXECUTE EXISTS
EXIT EXPLAIN EXPORT EXTENDED
EXTERN EXTERNAL FALSE FETCH
FIELDS FILE FILEFORMAT FILTER
FIRST FIXED_BINARY FLATTEN FLATTEN_JSON_ARRAY
FLOAT FOLLOWING FOR FOREACH
FOREIGN FORMAT FORMATTED FREE
FROM FULL FUNCTION FUNCTIONS
GENERATE GET GLOBAL GPATH
GPATH_QUERY GQL GQUERY GRANT
GRAPH GRAPHSQL GROUP GROUPING
GSHELL HANDLER HARD HASH_PARTITION
HAVING HEADER HELP HOLD
HOLD_DDLTIME HOST_GRAPH HOUR ICON
IDENTIFIED IDENTITY IDXPROPERTIES IF
IGNORE IGNORE_IF_EXISTED IGNORE_IF_EXISTS ILLUSTRATE
IMMEDIATE IMPORT IN INCREMENTAL
INDEX INDEXES INDICATOR INIT
INNER INOUT INPATH INPUT
INPUTDRIVER INPUTFORMAT INPUT_LINE_FILTER INSENSITIVE
INSERT INSTALL INT INT16
INT32 INT32_T INT64_T INT8
INTEGER INTERSECT INTERVAL INTO
INT_LIST INT_SET IS ITEMS
ITERATE JAR JOB JOIN
JSON KEY KEYS KEY_TYPE
KILL LANGUAGE LARGE LATERAL
LEADING LEAVE LEFT LESS
LIKE LIMIT LINES LOAD
LOADING LOCAL LOCALTIME LOCALTIMESTAMP
LOCATION LOCK LOCKS LOGICAL
LONG LOOP LS MACRO
MAP MAPJOIN MATCH MATCHES
MATERIALIZED MAX MERGE METHOD
MIN MINUS MINUTE MKDIR
MODIFIES MODULE MONTH MSCK
MULTISET MV NATIONAL NATURAL
NCHAR NCLOB NEW NO
NONE NOSCAN NOT NO_DROP
NULL NUMERIC OF OFFLINE
OLD ON ONLINE_POST ONLY
ONSCHEMA OPEN OPTIMIZE OPTION
OR ORDER OUT OUTER
OUTPUT OUTPUTDRIVER OUTPUTFORMAT OVER
OVERLAPS OVERWRITE OWNER PARALLEL
PARAMETER PARTIALSCAN PARTITION PARTITIONED
PARTITIONS PERCENT PIG PIGDUMP
PIGSTORAGE PLUS PRECEDING PRECISION
PREPARE PRESERVE PRETTY PRIMARY
PRIMARY_ID PRINCIPALS PROCEDURE PROTECTION
PURGE PWD QUERY QUIT
QUOTE RANGE RANGE_PARTITION READ
READONLY READS REAL REBUILD
RECORDREADER RECORDWRITER RECURSIVE REDUCE
REF REFERENCES REFERENCING REFRESH
REGEXP REGISTER RELEASE RENAME
REPAIR REPEAT REPLACE RESIGNAL
RESTRICT RESULT RETURN RETURNS
REVERSE_EDGE REVOKE REWRITE RIGHT
RLIKE RM RMF ROLE
ROLES ROLLBACK ROLLUP ROW
ROWS RUN SAMPLE SAVEPOINT
SCHEMA SCHEMAS SCHEMA_CHANGE SCOPE
SCROLL SEARCH SECOND SECONDARY_ID
SELECT SEMI SENSITIVE SEPARATOR
SERDE SERDEPROPERTIES SERVER SESSION_USER
SET SETS SHARED SHIP
SHOW SHOW_DATABASE SIGNAL SIMILAR
SIZE SKEWED SMALLINT SOME
SORT SORTED SPECIFIC SPECIFICTYPE
SPLIT SQL SQLEXCEPTION SQLSTATE
SQLWARNING SSL START START_ID
STATIC STATISTICS STATS STDERR
STDIN STDOUT STORE STORED
STREAM STREAMTABLE STRING STRING_LIST
STRING_SET STRUCT SUBMULTISET SUM
SYMMETRIC SYSTEM SYSTEM_USER TABLE
TABLES TABLESAMPLE TBLPROPERTIES TEMPORARY
TEMP_TABLE TERMINATED TEXTLOADER THEN
THROUGH TIME TIMESTAMP TIMEZONE_HOUR
TIMEZONE_MINUTE TINYINT TO TOKENIZE
TOKEN_LEN TOUCH TO_FLOAT TO_INT
TRAILING TRANSACTIONS TRANSFORM TRANSLATION
TREAT TRIGGER TRUE TRUNCATE
TUPLE TYPE TYPEDEF UDF_PARTITION
UINT UINT16 UINT32 UINT32_T
UINT64_T UINT8 UINT_SET UNARCHIVE
UNBOUNDED UNDIRECTED UNDO UNION
UNIONTYPE UNIQUE UNIQUEJOIN UNKNOWN
UNLOCK UNNEST UNSET UNSIGNED
UNTIL UPDATE UPSERT URI
USE USING UTC UTCTIMESTAMP
VAL VALUE VALUES VALUE_TYPE
VARCHAR VARYING VECTOR VERSION
VERTEX VIEW VOID WHEN
WHENEVER WHERE WHILE WINDOW
WITH WITHIN WITHOUT YEAR
CURRENT_DEFAULT_TRANSFORM_GROUP CURRENT_TRANSFORM_GROUP_FOR_TYPE
INT32_INT32_KV_LIST UINT32_UDT_KV_LIST
UINT32_UINT32_KV_LIST

Appendix B – GSQL Start-to-End Process and Data Flow

The figures below illustrates the sequence of steps and the dependencies to progress from no graph to a loaded graph and a query result, for TigerGraph platform version 0.8 and higher.  Note that online and offline follow the same flow.




Figure B1: Complete GSQL Workflow