Version 2.1 to 2.3. Copyright © 2015-2019 TigerGraph. All Rights Reserved.
Use the following command:
$ gsql --version
To see the version numbers of individual components of the platform:
$ gadmin version
Each release comes with documentation addressing how to perform an upgrade. Contact firstname.lastname@example.org for help in your specific situation. As of this writing (May 2018), the TigerGraph Platform is releasing version 2.0, which is a major revision.
If you correctly installed the system and are now logged in as the TigerGraph system user, you should be able to enter the GSQL shell by typing the
gsql command from an operating system prompt. If this command has never worked, then probably the installation was not successful. If it works but you are not sure what to do next, please see the GSQL Demo Examples guide.
If you believe you have installed the system correctly (e.g., you followed the TigerGraph Platform Installation Guide and received no errors, and the
gadmin commands are now recognized), then please contact email@example.com and summarize your issue in the email subject.
Different servers are needed for different purposes, but the TigerGraph should automatically turn services on and off as needed. Please be sure that the Dictionary (dict) server is on when using the TigerGraph system:
To check the status of servers:
$ gadmin status
Yes. For the GSQL shell and language, first enter the shell (type
gsql from an operating system prompt). Then type the
help command, e.g.,
This gives you a short list of commands. Note that "help" itself is one of the listed commands; there are help options to get more details about
QUERY commands. For example,
lists the command syntax for queries. See the "System Basics" section of the GSQL Language Reference, Part 1: Defining Graphs and Loading Data. The
gadmin administration tool also has a help menu and a manual page:
$ gadmin -h
$ man gadmin
User-defined identifiers are case-sensitive. For example, the names
user are different. The GSQL language keywords (e.g., CREATE, LOAD, VERTEX) are not case-sensitive, but in our documentation examples, we generally show keywords in ALL CAPS to make them easy to distinguish.
An identifier consists of letters, digits, and the underscore. Identifiers may not begin with a digit. Identifiers are case sensitive. Special naming rules apply to accumulators (see the Query section).
The general rule is that string literals within the GSQL language are enclosed in double quotation marks. For data that is to be imported (not yet in the GSQL data store), the GSQL loading language lets the user specify how data fields are delimited within your input files. The loading language has an option to specify whether single quotes or double quotes are used to mark strings. For more help on loading, see the "Loading Data" section of this document or of the GSQL Language Reference, Part 1: Defining Graphs and Loading Data .
Yes. You can create a text file containing a sequence of GSQL commands and then execute that file. To execute from outside the shell:
To execute the command file from within the shell:
See also the "Language Basics" and "System Basics" sections of the GSQL Language Reference, Part 1: Defining Graphs and Loading Data document.
Yes. Normally, an end-of-line character triggers execution of a line. You can use the
ENDkeywords to mark off a multi-line block of text that should not be executed until
This is an example of a loading statement split into multiple lines using BEGIN and END:
A TigerGraph graph schema consists of (A) one or more vertex types, (B) one or more edge types, and (C) a graph type. Each edge type is defined to be either DIRECTED or UNDIRECTED. The graph type is simply the list of vertex types and edges types which may exist in the graph. For more: See the section "Defining a Graph Schema" in the GSQL Language Reference, Part 1: Defining Graphs and Loading Data . Below is an example of a graph schema containing two vertex types, one edge type, and one graph type:
Alternately, a generic CREATE GRAPH statement can be used:
Each attribute of a vertex or edge has an assigned data type. v0.8 of the TigerGraph adds support for many more attribute types.: DATETIME, UDT, and container types LIST, SET, and MAP. The following is an abbreviated list. For a complete list and description, see the section "Attribute Data Types" of the GSQL Language Reference, Part 1: Defining Graphs and Loading Data .
Starting with v1.2, the TigerGraph MultiGraph service, an add-on option, supports logical partitions of one unified master graph. Each partition is treated as an independent graph, with its own set of user privileges. Graphs can overlap, to create a shared data space.
For performance reasons, we recommend to keep the number of different vertex and edge types under 5,000. The upper limit for the number of different vertex and edge types is approximately 10,000, depending on the complexity of the types.
From within the GSQL Shell, the
ls command lists the catalog : the vertex type, edge type, and graph type definitions, job definitions, query definitions, and some system configuration settings. If you have not set your active graph, then
ls will show only item which have global scope. To see graph-specific items (including loading jobs and queries), you must define an active graph.
The GSQL language includes ADD, ALTER, and DROP commands. See the section "Update Your Data" in the GSQL Demo Examples or the section "Modifying a Graph Schema" in the GSQL Language Reference, Part 1: Defining Graphs and Loading Data for details. Note that altering the graph schema will invalidate your old data loading and query jobs. You should create and install new loading and query jobs.
To delete your entire catalog, containing not just your vertex, edge, and graph type definitions, but also your loading job and query definitions, use the following command:
To delete just your graph schema, use the DROP GRAPH command:
DROP GRAPH g1
UPDATE Deleting the graph schema also erases the contents of the graph store. To erase the graph store without deleting the graph schema, use the following command:
CLEAR GRAPH STORE
See also " How do I erase all data? "
See the GSQL Demo Examples for introductory examples. See GSQL Language Reference, Part 1: Defining Graphs and Loading Data for the complete specifications. We also have a cheatsheets: see GSQL Cheatsheets.
Which loading method should I use?
Created 01 May 2018
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.
The syntax for pre-v2.0 online loading and offline loading will be supported through v2.x, but they are deprecated.
Why has Offline loading been deprecated?
Online loading is preferred. Online loading can do everything that offline loading can do, plus it has the following advantages:
Can run while other graph operations are in progress.
Uses multithreaded execution for faster performance.
Does not need to turn the GPE off, which saves time.
Its data source is specified at run time rather than at compile time.
Can add data to an existing graph.
Offline loading is deprecated and is now being emulated by online loading. Therefore, there is no performance difference.
Here are the main differences between the styles. Note that v2.0 is superficially similar to old offline loading, but it also supports run-time filenames. The actual behavior and performance of v2.0 loading is online loading with concurrency.
See the offline2online command, described in GSQL Language Reference, Part 1: Defining Graphs and Loading Data .
The GSQL data loader reads text files organized in tabular or JSON format . Each field may represent numeric, boolean, string, or binary data. Each data field may contain a single value or a list of values (see How do I split a data field containing a list of values into separate vertices and edges? ).
Each tabular input data file should be structured as a table, in which each line represents a row, and each row is a sequence of data fields, or columns. A data field can contain string or numeric data. To represent boolean values, 0 or 1 is expected. A header line may be included, to associate a name with each column. A designated character separates columns. For example, if the designated separator character is the comma, this format is commonly called CSV, for Comma-Separated Values. Below is an example of a CSV file with a header. The
uidcolumn is int type,
nameis string type,
avg_scoreis float type, and
is_memberis boolean type. See simple examples in Real-Life Data Loading and Querying Examples and a complete specification in the section "Creating a Loading Job" in GSQL Language Reference, Part 1: Defining Graphs and Loading Data .
The data field (or token ) separator can be any single ASCII character, including one of the non-printing characters. The separator is specified with the SEPARATOR phrase in the USING clause. For example, to specify the semicolon as the separator:
To specify the tab character, use
\t. To specify any ASCII character, use
nn is the character's ASCII code, in decimal. For example, to specify ASCII 30, the Record Separator (RS):
TigerGraph does not require fields to be enclosed in quotation marks, but is it recommended for string fields. If the QUOTE option is enabled, and if the loader finds a pair of quotation marks, then the loader treats the text within the quotation marks as one value, regardless of any separation characters that may occur in the value. The user must specify whether strings are marked by single quotation marks or double quotation marks.
For example, if
QUOTE="double" are set, then when the following data are read,
"Lee, Tom" will be read as a single field. The comma between Lee and Tom will not separate the field.
No. You must specify either
The following three parameters should be considered for every loading job from a tabular input file:
The next two parameters, FILENAME and EOL are required if the job is an ONLINE_POST job:
All of the these five parameters are combined into one USING clause with a list of parameter/value pairs. The parameters may appear in any order.
The location of the USING clause depends on whether the job is an offline loading job or an online loading job. For offline loading, the USING clause appears at the end of the LOAD statement. For example:
For online loading, the USING clause appears at the end of the RUN statement
You can define a header line (a sequence of column names) within a loading job using a DEFINE HEADER statement, such as the following:
This statement must appear before the LOAD statement that wishes to use the header definition. Then, the LOAD statement must set the USER_DEFINED_HEADER parameter in the USING clause. A brief example is shown below:
Input data fields can always be referenced by position. They can also be referenced by name, if a header has been defined.
Position-based reference: The leftmost field is
$0, the next one is
$1, and so on.
$"name" , where
name is one of the header column names.
For example, if the header is
then the third field can be referred to as either
First, to clarify the task, consider a graph schema with two vertex types, Book and Genre, and one edge type, book_genre:
Further, each row of the input data file contains three fields: bookcode , title , and genres , where genres is a list of strings associated with the book. For example, the first few lines of the data file could be the following:
The data line for bookcode 101 should generate one Book instance ("Harry Potter and the Philosopher's Stone"), four Genre instances ("fiction", "adventure", "fantasy", "young adult"), and four Book_Genre instances, connecting the Book instance to each of the Genre instances. This process of creating multiple instances from a list field (e.g., the genres field) is called flattening .
To flatten the data, we use a two-step load. The first LOAD statement uses the flatten() function to split the multi-value field and stores the results in a TEMP_TABLE. The second LOAD statement takes the TEMP_TABLE contents and writes them to the final edge type.
The flatten function has three arguments: (field_to_split, separator, number_of_parts_in_one_field). In this example, we want to split $2 (genres), the separator is the comma, and each field has only 1 part. So, the flatten function is called with the following arguments:
flatten($2, ",",1) . Using the example of data file , TEMP_TABLE t1 will then contain the following:
The second LOAD statement uses the TEMP_TABLE t1 to generates Genre vertex instances and book_genre_instances. While there are 7 rows shown in the sample TEMP_TABLE, only 6 Genre vertices will be generated, because there are only 6 unique values;
"Fiction" appears twice. Seven book_genre edges will be generated, one for each row in the TEMP_TABLE.
There is another version of the flatten function which has four arguments and which supports a two-level grouping. That is, the field contains a list of groups, each group composed of N subfields. The arguments are (field_to_split, group_separator, sub_field_separator, number_of_parts_in_one_group). For example, suppose the data line were organized this way instead:
Then the following loading statements would be appropriate:
Yes. Use online loading. Specifically, online loading lets you define a general loading process without naming the data source. Every time you call an online loading job, you name the source file. It can be a different file each time, or it can be the same file, if the contents of the file are changing over time. Also, if it happens that the loader re-reads a data line that it has encountered before, it will just reload the data (except for container attributes, e.g., a LIST attribute, using a reduce() loading function. In that case, there is an accumulative effect for re-reading a data line).
The GSQL Loading includes some built-in token functions (a token is one column or field of a data input line.) A user can also define custom token functions. Please see the section "Built-In Loader Token Functions" in the GSQL Language Reference, Part 1: Defining Graphs and Loading Data .
No. One of the advantages of the TigerGraph loading system is the flexible relationship between input files and resulting vertex and edge instances. In general, there is a many-to-many relationship: one input file can generate many vertex and edge types.
From the LOAD statement perspective for a online loading job:
Each LOAD statement refers to one input file.
Each LOAD statement can have one or more resulting vertex types and one or more resulting edge types.
Hence, one LOAD statement can potentially describe the one-to-many mapping from one input file to many resulting vertex and edge types.
It is not necessary for every input line to always generate the same set of vertex types and edge types. The WHERE clause in each TO VERTEX | TO EDGE clause can be used to selectively choose and filter which input lines generate which resulting types.
If there is already data in the graph store and you wish to insert more data, you have a few options. First, if you have bulk data stored in a file (local disk, remote or distributed storage), you can us e Online Loading .
Second, if you have a few specific insertions, you can use the Upsert da ta command in the RESTPP API User Guide . For Upsert, the data must be formatted in JSON format.
Third, you can write a query containing INSERT statements. The syntax is similar to SQL INSERT. (See GSQL Language Reference Part 2 - Querying . ) The advantage of query-based INSERT is that the details (id values and attribute values) can be determined at run time and even can be based on an exploration and analysis of the existing graph. The disadvantage is that the query-insert job must be compiled first and data values must either be hardcoded or supposed as input parameters.
You can modify the schema in several ways:
Add new vertex or edge types
Drop existing vertex or edge types
Add or drop attributes from an existing vertex or edge type
See the section "Modifying a Graph Schema" in GSQL Language Reference Part 1 - Defining Graphs and Loading Data .
To make a known modification of a known vertex or edge: Option 1) Make a RESTPP endpoint request, to the POST /graph or DELETE /graph endpoint. See the RESTPP API User Guide .
Option 2) The Loading language includes an upsert command. The UPSERT statement performs a combined modify-or-add operation, depending on whether the indicated vertex or edge already exists. Examples of UPSERT are described in the GSQL Demo Examples document. The GSQL Language Reference Part 1 - Defining Graphs and Loading Data provides a full specification .
Option 3) The query language now includes an UPDATE statement which enables sophisticated selection of which vertices and edges to update and how to update them. Likewise, there is an INSERT statement in the query language. See the GSQL Language Reference Part 2 - Querying .
You can write a query which selects vertices or edges to be deleted. See the DELETE subsections of the "Data Modification Statements" section in GSQL Language Reference Part 2 - Querying .
If you wish to completely clear all the data in the graph store, use the
CLEAR GRAPH STORE -HARD command. Be very careful using this command; deleted data cannot be restored (except from a Backup). Note that clearing the data does not erase the catalog definitions of vertex, edge, and graph types. See also " How do I delete my entire graph schema? "
-HARD must be in all capital letters.
Yes. The GSQL Query Language is a full-featured graph query-and-data-computation language. In addition, there is a small lightweight set of built-in query commands that can inspect the set of stored vertices and edges, but these built-in commands do not support graph traversal (moving from one vertex to another via edges). We refer to this as the Standard Data Manipulation API or the Built-in Query Language (described in RESTPP API User Guide and the GSQL Demo Examples )
For a first-time user: See the documents GSQL Demo Examples and then GSQL Language Reference Part 2 - Querying . For users with some experience, a reference card is now available: GSQL Query Language Reference Card.
The GSQL Query Language supports powerful graph querying, but it is also designed to perform powerful computations. GSQL is Turing-complete, so it can be considered a programming language. It can be used for simple SQL-like queries, but it also features control flow (IF, WHILE, FOREACH), procedural calls, local and global variables, complex data types, and accumulators to enable much more sophisticated use.
Three new types were introduced in v0.8: GroupByAccum, BitwiseAndAccum, and BitwiseOrAccum. Version 0.8.1. added ArrayAccum. This is a quick summary. For a more detailed explanation, see the "Accumulator Types" section of GSQL Language Reference Part 2 - Querying .
In the following table, baseType means any of the following: INT, UINT, FLOAT, DOUBLE, STRING, BOOL, VERTEX, EDGE, JSONARRAY, JSONOBJECT, DATETIME
See the section "Accumulators" in the GSQL Language Reference Part 2 - Querying document.
Vertex and edge IDs (i.e., the unique identifier for each vertex or edge) are treated differently than user-defined attributes. Special keywords must be used to refer to the PRIMARY_ID, FROM, or TO id fields.
In a CREATE VERTEX statement, the PRIMARY_ID is required and is always listed first. User-defined attributes are optional and come after the required ID fields.
In a built-in query, if you wish to select vertices by specifying an attribute value, you use the attribute name (e.g., title):
In contrast, if you wish to reference vertices by the id value, the lowercase keyword
primary_id must be used. Note that that query does not use the id name
In a CREATE EDGE statement, the FROM and TO vertex identifiers are required and are always listed first. The FROM and TO values should match the PRIMARY_ID values of a source vertex and a target vertex. In the example below,
date_time are user-defined optional attributes.
In a query, if you wish to select edges by specifying their FROM or TO vertex values, you must use the lowercase keywords from_id or to_id .
The data are in JSON format. See the section "Output Statements" in the GSQL Language Reference Part 2 - Querying .
Yes. The maximum output size for a query is 2GB. If the result of a query would be larger than 2GB, the system may return no data. No error message is returned.
Also, for built-in queries (using the Standard Data Manipulation REST API), queries return at most 10240 vertices or edges.
INSTALL QUERY query_name is required for each GSQL query, after its initial CREATE QUERY query_name statement and before using RUN QUERY query_name . After INSTALL query has been executed, RUN QUERY can now be used.
Anytime after INSTALL QUERY, another statement, INSTALL QUERY -OPTIMIZE can be executed once. This operation optimizes all previously installed queries, reducing their run times by about 20%.
Optimize a query if query run time is more important to you than query installation time. The initial INSTALL QUERY operation runs quickly. This is good for the development phase.
The optional additional operation INSTALL QUERY -OPTIMIZE will take more time, but it will speed up query run time. This makes sense for production systems.
In short, yes. They will not be executed at the same time, but the installations will be queued by the order in which they were received.
Yes. A ListAccum is like an array, a 1-dimensional array. If you nest ListAccums as the elements within an outer ListAccum, you have effectively made a 2-dimensional array. Please read Section "Nested Accumulators" in the GSQL Language Reference Part 2 - Querying for more details. Here is an example:
Yes, please read Section "Nested Accumulators" in the GSQL Language Reference Part 2 - Querying for more details. There are seven types of container accumulators: ListAccum, SetAccum, BagAccum, MapAccum, ArrayAccum HeapAccum, and GroupByAccum. Here the allowed combinations:
ListAccum can contain ListAccum.
MapAccum and GroupByAccum can contain any container accumulator except HeapAccum.
ArrayAccum is always nested.
Here is an example:
To write a loading job, you must know the format of the input data files, so that you can describe to GSQL how to parse each data line and convert it into vertex and edge attributes. To validate a loading job, that is, to check that the actual input data meet your expectations, and that they produce the expected vertices and edges, you can use two features of the RUN JOB command: the -DRYRUN option and loading a specified range of data lines.
The full syntax for an (offline) loading job is the following:
RUN JOB [-DRYRUN] [-n [
-DRYRUN option will read input files and process data as instructed by the job, but it does not store data in the graph store.
-n option limits the loading job to processing only a range of lines of each input data file. The selected data will be stored in the graph store, so the user can check the results. 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.
The following command lists the log locations of the log files:
If the platform has been installed with default file locations, so that <TigerGraph_root_dir> = /home/tigergraph/tigergraph, then the output would be the following:
GPE : general system performance logs. GSE : Graph services logs. RESTPP : REST API call logs. GSQL : General GSQL logs.
Each loading run creates a log file, stored in the folder <TigerGraph_root_dir>/dev/gdk/gsql/output. The filename load_output.log is a link to the most recent log file. This file contains summary statistics on the number of lines read, the vertices created, and various types of errors encountered. Or, you can type a shell command to find log paths "gadmin log".
The log files record detailed internal operations and state information in response to user actions. They provide vital information for diagnosing and debugging your system. All log files can be found in the /home/tigergraph/tigergraph/logs directory. Through typing the command gadmin log, you will be given all the file paths of the most commonly used log files.
GPE Logs - Graph Processing Engine Logs GSE Logs - Graph Storage Engine Logs GSQL Logs - System & Query Logs RESTPP Logs - API call Logs NGINX Logs - HTTP Request Logs VIS Logs - GraphStudio Logs