Creating a Loading Job
After a graph schema 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 that 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 a RUN LOADING JOB statement. These two statements, and the components of the loading job, are detailed below.
The structure of a loading job will be presented hierarchically, top-down:
CREATE LOADING JOB, which may contain a set of DEFINE and LOAD statements
  • DEFINE statements
  • LOAD statements, which can have several clauses
All blank spaces are meaningful in string fields in CSV and JSON. Either pre-process your data files to remove extra spaces, or use GSQL's token processing functions gsql_trim, gsql_ltrim, and gsql_rtrim (Built-in Loader Token Functions).

Loading job capabilities

TigerGraph's syntax for defining and running loading jobs 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. 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 FileLoader.ReplicaNumber.
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.
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 the loading job, which you can call to load data into your graph as an alternative to RUN LOADING JOB.
Example loading jobs and data files for the book_rating schema defined earlier in the document are available in the $(gadmin config get System.AppRoot)/document/examples folder in your TigerGraph platform installation.

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 LOADING JOB syntax
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CREATE LOADING JOB job_name FOR GRAPH graph_name {
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[zero or more DEFINE statements;]
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[zero or more LOAD statements;] | [zero or more DELETE statements;]
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}
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LOAD or DELETE Statements ****A loading job may contain either LOAD or DELETE statements but not both. A loading job that includes both will be rejected when the CREATE statement is executed.

Example:

Loading Job Example
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CREATE LOADING JOB job1 FOR GRAPH graph1 {
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# File path specified at complile time
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DEFINE FILENAME file1 = "/data/v1.csv";
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DEFINE FILENAME file2;
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LOAD file1 TO VERTEX v1 VALUES ($0, $1, $2);
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LOAD file2 TO EDGE e2 VALUES ($0, $1);
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}
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# Run-time specification will override path specified at compile time
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RUN LOADING JOB job1 USING file1="m1:/data/v1_1.csv", file2="m2:/data/e2.csv"
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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:
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DROP JOB ALL
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DROP JOB *
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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 DEFINE 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.
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DEFINE FILENAME filevar ["=" filepath_string ];
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filepath_string = ( path | " all :" path | " any :" path | mach_aliases " :" path ["," mach_aliases ":" path ]* )
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mach_aliases = name["|"name]*
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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 _:
  • 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
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"/data/graph.csv"
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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 $sys.data_root.
Example: using sys.data_root in a loading job
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CREATE LOADING JOB filePathEx FOR GRAPH gsql_demo {
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LOAD "$sys.data_root/persons.csv" TO ...
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}
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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
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SET sys.data_root="/data/mydata"
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RUN JOB filePathEx
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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.
  • "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. If the path is invalid on any of the machines, the job will be aborted. Also, the session parameter $sys.data_root may not be used.
ALL:path examples
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"ALL:/data/graph.csv"
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  • "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 the specified path. **If the path is invalid on any of the machines, those machines where the path is not valid are skipped. **Also, the session parameter $sys.data_root may not be used.
ANY:path examples
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"ANY:/data/graph.csv"
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  • 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
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"m1:/data1.csv, m2|m3|m5:/data/data2.csv"
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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.
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DEFINE HEADER header_name = " column_name "[," column_name "]*;
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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.
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DEFINE INPUT_LINE_FILTER filter_name = boolean_expression_using_column_variables ;
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LOAD statement

A LOAD statement describes 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 Knowledge Base and FAQs_** **_and the tutorials, such as GSQL 101, 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.

Syntax

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LOAD [filepath_string|filevar|TEMP_TABLE table_name ]
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Destination_Clause [, Destination_Clause ]*
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[TAGS clause ]
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[USING clause ];
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filevar_ _must have been previously defined in a DEFINE FILENAME statement.
filepath_string_ _must satisfy the same rules given above in the DEFINE FILENAME section.

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
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TO VERTEX vertex_type_name VALUES (id_expr [, attr_expr]*)
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[WHERE conditions] [OPTION (options)]
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Edge Destination Clause
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TO EDGE edge_type_name VALUES (source_id_expr [source_type_expr],
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target_id_expr [target_type_expr]
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[, attr_expr]*)
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[WHERE conditions] [OPTION (options)]
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For the TO VERTEX and TO EDGE destination clauses, the following rules for its parameters apply:
  • 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.
  • For edge clauses, the source_id_expr and target_id_expr can each optionally be followed by a source_type_expr and target_type_expr, respectively. The source_type_expr and target_type_expr must evaluate to one of the allowed endpoint vertex types for the given edge type. By specifying the vertex type, this tells the loader what id types to expect. This may be important when the edge type is defined to accept more than one type of source/target vertex.
TEMP_TABLE Destination Clause
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TO TEMP_TABLE table_name (id_name [, attr_name]*)
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VALUES (id_expr [, attr_expr]*)
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[WHERE conditions] [OPTION (options)]
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The TO TEMP_TABLE clause defines 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.
For fast loading of edge data, referential integrity checking is disabled by default. For an edge to be valid, it must refer to endpoint vertices that exist. To support fast, out-of-order loading,** if one or both of the endpoint vertices do not yet exist, the loader will create vertices with the necessary IDs and default attribute values.** Due to the loader's UPSERT semantics, if the vertex data is loaded later, it will be automatically merged with the dummy vertices. The user can disable this feature and perform regular referential integrity checking by setting the VERTEX_MUST_EXIST=true option.

Examples

Suppose we have the following vertex and edge types:
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CREATE VERTEX Person (pid STRING PRIMARY KEY, birthdate DATETIME)
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CREATE VERTEX Company (cid INT PRIMARY KEY, industry STRING)
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CREATE DIRECTED EDGE Visit (FROM Person, TO Person
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| FROM Person, TO Company, year INT)
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A Visit edge can connect two Person vertices or a Person to a Company. A Person has a string ID, while a Company has an INT ID. Then suppose the Visit edge source data comes from a single CSV file, containing both variants of edges. Note that the 2nd column ($1) contains either Person or Company, and that the 3rd column ($2) contains either a string or an integer.
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Sam,Person,Joe,2012
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Sam,Company,4057,2017
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Chris,Company,9401,2016
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Pat,Person,Taylor,2020
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Using the optional target_type_expr field, we can load both variants of the Visit edge with a single clause.
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LOAD file1 TO EDGE Visit VALUES ($0, $2 $1, $3) USING separator=",";
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Known issue: you must include a USING clause when loading data into edge types with different FROM-TO vertex pairs, even if all options are default.

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 $1, and so on.
By Name: Columns can be named, either through a header line in the input file, or through a DEFINE HEADER statement. If a header line is used, then the first line of the input file should be structured like a data line, using the same separator characters, except that each column contains a column name string instead of a data value. Names are enclosed in double quotes, e.g. quot;age".
**Data file name: **$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
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LOAD "xx/yy/a.csv" TO VERTEX person VALUES ($0, $1, $sys.file_name)
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  • 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).
Users do not need to explicitly define a primary ID. Given the attributes, one will be selected as the primary key.

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.
  • 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. 1.
    UINT: Any non-digit character. (Out-of-range values cause overflow instead of rejection)
  2. 2.
    INT: Any non-digit or non-sign character. (Out-of-range values cause overflow instead of rejection)
  3. 3.
    FLOAT and DOUBLE: Any wrong format
  4. 4.
    STRING, STRING COMPRESS, FIXED_BINARY: N/A
  5. 5.
    DATETIME: Wrong format, invalid date time, or out of range.
  6. 6.
    BOOL: Any value not listed later.
  7. 7.
    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.
  • **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.
  • 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. 1.
    The attribute values of the new object overwrite the attribute values of the existing data object.
  2. 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
  • 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,
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LOAD TO VERTEX person VALUES ($0, $1, _, $2)
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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. 1.
    If the load operation is creating a new vertex or edge, then the skipped attribute will be assigned the default value.
  2. 2.
    If the load operation 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 | quot;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
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[sign][digits].[digits](e|E)[sign][digits]
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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
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# Valid floating point values
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-198256.03
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+16.
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-.00036
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7.14285e15
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9.99E-22
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8
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# Invalid floating point values
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-198,256.03
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9.99 E-22
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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 %d represent month (1 to 12) and 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 BOOL Attribute

When loading data from CSV files the following values are accepted for BOOL attributes :
  • True: TRUE,True,true,1
  • False: FALSE,False,false,0
When loading data from JSON documents, the valid BOOL values are true and false.

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
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TYPEDEF TUPLE <f1 INT (1), f2 UINT, f3 STRING (10), f4 DOUBLE > myTuple # define a UDT
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CREATE VERTEX v_udt (id STRING PRIMARY KEY, att_udt myTuple)
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CREATE GRAPH test_graph (v_udt)
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CREATE LOADING JOB load_udt FOR GRAPH test_graph {
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DEFINE FILENAME f;
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LOAD f TO VERTEX v_udt VALUES ($0, myTuple($1, $2, $3, $4) );
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# $1 is loaded as f1, $2 is loaded as f2, and so on
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}
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RUN LOADING JOB load_udt USING f="./udt.csv"
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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 that 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
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CREATE VERTEX test_vertex (PRIMARY_ID id STRING, iset SET<INT>, ilist LIST<INT>)
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CREATE UNDIRECTED EDGE test_edge(FROM test_vertex, TO test_vertex)
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CREATE GRAPH test_set_list (*)
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CREATE LOADING JOB load_set_list FOR GRAPH test_set_list {
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DEFINE FILENAME f;
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LOAD f TO VERTEX test_vertex VALUES ($0, $1, $1);
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}
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RUN LOADING JOB load_set_list USING f="./list_set_vertex.csv"
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list_set_vertex.csv
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list_set_vertex.csv
list_set_vertex.csv
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1,10
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3,30
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1,20
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3,30
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3,40
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1,20
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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
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CREATE VERTEX v_set (PRIMARY_ID id STRING, nick_names SET<STRING>)
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CREATE VERTEX v_list (PRIMARY_ID id STRING, lucky_nums LIST<INT>)
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CREATE GRAPH test_graph (*)
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CREATE LOADING JOB load_set_list FOR GRAPH test_graph {
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DEFINE FILENAME f;
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LOAD f TO VERTEX v_set VALUES ($0, SET($1,$2,$3) );
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LOAD f TO VERTEX v_list VALUES ($0, LIST($2,$4) );
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}
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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 throughout the whole file. Below shows an example:
Example: SET/LIST loading by SPLIT() example
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CREATE VERTEX test_vertex (PRIMARY_ID id STRING, ustrset SET<STRING>, ilist LIST<INT>)
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CREATE UNDIRECTED EDGE test_edge(FROM test_vertex, TO test_vertex)
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CREATE GRAPH test_split (*)
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CREATE LOADING JOB set_list_job FOR GRAPH test_split {
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DEFINE FILENAME f;
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LOAD f TO VERTEX test_vertex VALUES ($0, SPLIT($1,"|") , SPLIT($2,"#") );
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}
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RUN LOADING JOB set_list_job USING f="./split_list_set.csv"
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split_list_set.csv
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split_list_set.csv
split_list_set.csv
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vid,names,numbers
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v1,mike|tom|jack, 1 # 2 # 3
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v2,john, 5 # 4 # 8
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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 that 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
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CREATE VERTEX v_map (PRIMARY_ID id STRING, att_map MAP<INT, STRING>)
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CREATE GRAPH test_graph (*)
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CREATE LOADING JOB load_map FOR GRAPH test_graph {
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DEFINE FILENAME f;
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LOAD f TO VERTEX v_map VALUES ($0, ($1 -> $2) );
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}
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**** 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
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CREATE VERTEX v_map (PRIMARY_ID id STRING, att_map MAP<INT, STRING>)
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CREATE GRAPH test_graph (*)
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CREATE LOADING JOB load_map FOR GRAPH test_graph {
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DEFINE FILENAME f;
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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.
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}
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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
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one_key_value.csv
example data with one key-value pair per line
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vid,key_value
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v1,1:mike
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v2,2:tom
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v1,3:lucy
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multi_key_value.csv
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multi_key_value.csv
example data with multiple key-value pairs per line
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vid,key_value_list
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v1,1:mike#4:lin
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v2,2:tom
4
v1,3:lucy#1:john#6:jack
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Loading a MAP by method 3: SPLIT() function
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CREATE VERTEX v_map (PRIMARY_ID id STRING, att_map MAP<INT, STRING>)
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CREATE GRAPH test_graph (*)
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CREATE LOADING JOB load_map FOR GRAPH test_graph {
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DEFINE FILENAME f;
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LOAD f TO VERTEX v_map VALUES ($0, SPLIT($1, ":", "#") );
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}
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The SPLIT() function cannot be used for UDT type elements.

Loading Composite Key Attributes

Loading a Composite Key for a vertex works no differently than normal loading. Simply load all the attributes as you would for a vertex with a single-attribute primary key. The primary key will automatically be constructed from the appropriate attributes.
When loading to an edge where either TO_VERTEX or FROM_VERTEX contains a composite key, the composite set of attributes must be enclosed in parentheses. See the example below.
Example: loading composite key to vertex and edge
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#schema setup
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CREATE VERTEX compositePerson (id uint, name string, PRIMARY KEY (name,id))
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CREATE VERTEX compositeMovie (id uint, title string, country string, year uint, primary key (title,year,id))
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CREATE DIRECTED EDGE compositeRoles (from compositePerson,to compositeMovie, role string) with reverse_edge="composite_roles_reverse"
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CREATE GRAPH MyGraph(*)
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#loading job
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CREATE LOADING JOB composite_load FOR GRAPH MyGraph {
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LOAD "$sys.data_root/movies.csv" TO VERTEX compositeMovie VALUES
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($"id", $"title", $"country" ,$"year") USING header ="true", separator=",";
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LOAD "$sys.data_root/persons.csv" TO VERTEX compositePerson VALUES
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($"id",$"name") USING header = "true", separator =",";
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LOAD "$sys.data_root/compositeroles.csv" TO EDGE compositeRoles VALUES
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(($"personName", $"personId"),($"movieTitle",$"movieYear",$"movieId"),$"role")
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USING header="true", separator = ",";
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}
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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
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#schema setup
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CREATE VERTEX user(PRIMARY_ID id UINT)
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CREATE VERTEX product(PRIMARY_ID id UINT)
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CREATE VERTEX picture(PRIMARY_ID id UINT)
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CREATE UNDIRECTED EDGE purchase (FROM *, TO *)
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CREATE GRAPH test_graph(*)
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#loading job
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CREATE LOADING JOB test2 FOR GRAPH test_graph {
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DEFINE FILENAME f;
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LOAD f
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TO EDGE purchase VALUES ($0 user, $1 product),
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TO EDGE purchase VALUES ($0 user, $2 picture);
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}
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Token Functions

Token functions are 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.
To use a token function, replace the attribute in the destination clause of the LOAD statement with the function call. The arguments of the function can be a column from the FILE object.

Example

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CREATE LOADING JOB loadOrders {
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DEFINE FILENAME f;
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LOAD f
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TO VERTEX order VALUES(gsql_trim($"id"), $"date");
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}
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Built-in token functions for attribute expressions

The following token functions can be used in an id or attribute expression
Function
Output type
Description
gsql_reverse( _in_string _)
string
Returns a string with the characters in the reverse order of the input string in_string.
gsql_concat( _string1, string2,...,stringN _)
string
Returns a string which is the concatenation of all the input strings.
gsql_uuid_v4()
string
Returns a version-4 UUID.
gsql_split_by_space( _in_string _)
string
Returns a modified version of in_string, in which each space character is replaced with ASCII 30 (decimal).
gsql_substring(str, beginIndex [, length])
string
Returns the substring beginning at beginIndex, having the given length.
gsql_find(str, _substr _)
int
Returns the start index of the substring within the string. If it is not found, then return -1.
gsql_length(_str _)
int
Returns the length of the string.
gsql_replace(str, oldToken, newToken [, max])
string
Returns the string resulting from replacing all matchings of oldToken with newToken in the original string. If a _max _count is provided, there can only be up to that many replacements.
gsql_regex_replace( str, regex, replaceSubstr )
string
Returns the string resulting from replacing all substrings in the input string that match the given regex token with the substitute string.
gsql_regex_match(str, regex )
bool
Returns true if the given string token matches the given regex token and false otherwise.
gsql_to_bool( _in_string _)
bool
Returns true if the _in_string _is either "t" or "true", with case insensitive checking. Returns false otherwise.
gsql_to_uint( _in_string _)
uint
If in_string is the string representation of an unsigned int, the function returns that integer. If in_string is the string representation of a nonnegative float, the function returns that number cast as an int.
gsql_to_int( _in_string _)
int
If in_string is the string representation of an int, the function returns that integer. If in_string is the string representation of a float, the function returns that number cast as an int.
gsql_ts_to_epoch_seconds( _timestamp _)
uint
Converts a timestamp in canonical string format to Unix epoch time, which is the int number of seconds since Jan. 1, 1970. Refer to the timestamp input format note below.
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 ( quot;array_name" )
flatten_json_array ( quot;array_name", quot;sub_obj_1", quot;sub_obj_2", ..., quot;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.
gsql_upper( _in_string _)
string
Returns the input string in upper-case.
gsql_lower( _in_string _)
string
Returns the input string in lower-case.
gsql_trim( _in_string _)
string
Trims whitespace from the beginning and end of the input string.
gsql_ltrim( in_string )
gsql_rtrim( in_string )
string
Trims white space from either the beginning or the end of the input string (Left or right).
gsql_year( timestamp )
int
Returns 4-digit year from timestamp. Refer to timestamp input format note below.
gsql_month( timestamp )
int
Returns month (1-12) from timestamp. Refer to timestamp input format note below.
gsql_day( timestamp )
int
Returns day (1-31) from timestamp. Refer to timestamp input format note below.
gsql_year_epoch( epoch )
int
Returns 4-digit year from Unix epoch time, which is the int number of seconds since Jan. 1, 1970.
gsql_month_epoch( epoch )
int
Returns month (1-12) from Unix epoch time, which is the int number of seconds since Jan. 1, 1970.
gsql_day_epoch( epoch )
int
Returns day (1-31) from Unix epoch time, which is the int number of seconds since Jan. 1, 1970.

Timestamp Input Format

The timestamp parameter should be in one of the following formats: "%Y-%m-%d %H:%M:%S" "%Y/%m/%d %H:%M:%S" "%Y-%m-%dT%H:%M:%S.000z" // text after the dot . is ignored

User-defined token functions

Users can write their own token functions in C++ and install them in the GSQL system. To learn how to add a user-defined token function, see Add a User-Defined Token Function.

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
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REDUCE( reducer_function ( input_expr ) )
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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 values cumulatively received STRING: concatenation of all 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 received INT, 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 operators can be used to express complex operations 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 test these data types for exact equality or inequality. Instead, one should allow for an acceptable amount of error. The following example checks if $0 = 5, with an error of 0.00001 permitted:
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WHERE to_float($0) BETWEEN 5-0.00001 AND 5+0.00001
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  • **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.

Other Optional LOAD Clauses

OPTION clause

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

TAGS clause (Beta)

The TAGS clause specifies the tags to be applied to the vertices loaded by the LOAD statement.
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TAGS "(" tag_name (, tag_name)* ")" BY [ OR | OVERWRITE ]
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If a LOAD statement has a TAGS clause, it will tag the vertices with the tags specified in the TAGS clause. Before vertices can be loaded and tagged with a LOAD statement, the vertex type must first be marked as taggable, and the tags must be defined.
Users have two options when it comes to how to merge tags if the target vertices exist in the graph:
  • BY OR : Add the new tags to the existing set of tags.
  • BY OVERWRITE: Overwrite existing tags with the new tags.

USING clause

A USING clause contains one or more optional parameter value pairs:
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USING parameter=value [,parameter=value]*
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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.
Parameter
Meaning of Value
Allowed Values
SEPARATOR
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
the end-of-line character
any ASCII sequence
Default = "\n" (system-defined newline character or character sequence)
QUOTE (See note below)
specifies explicit boundary markers for string tokens, either single or double quotation marks. See more details below.
"single" for ' "double" for "
HEADER
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"
USER_DEFINED_HEADER
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
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 Loading JSON Data section below)
whether each line is a json object (see Section "JSON Loader" below for more details)
"true", "false"
Default is "false"
NEW_VERTEX_ONLY
If true, treat vertices as insert-only. If the input data refers to a vertex which already exists, do not update it.
If false, upsert vertices.
"true", "false"
Default is "false"
VERTEX_MUST_EXIST
(See VERTEX_MUST_EXIST section below)
If true, only insert or update an edge If both endpoint vertices already exist. If false, always insert new edges, creating endpoint vertices as needed, using given id and default values for other parameters.
"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.
Previously, ill-formatted strings such as **a"a,b"ac,d **would be parsed as a,b,d ignoring a,a,c. The expected input string should be a,"a,b",ac,d. In v2.4, incorrectly formatted strings such as this example will be parsed normally, giving you this result: a"a,b"ac and d.

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 quot;abc":"def" is used to access "this_value". The double quotes are mandatory.
An example is shown below:
USING JSON_FILE test schema and loading job
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CREATE VERTEX encoding (PRIMARY_ID id STRING, length FLOAT default 10)
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CREATE UNDIRECTED EDGE encoding_edge (FROM encoding, TO encoding)
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CREATE GRAPH encoding_graph (*)
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CREATE LOADING JOB json_load FOR GRAPH encoding_graph {
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LOAD "encoding.json" TO VERTEX encoding
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VALUES ($"encoding", $"indent":"length") USING JSON_FILE="true";
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}
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RUN JOB json_load
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To specify an end-of-line character other than the standard one, use the EOL option, as shown below.
JSON loading using EOL
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CREATE LOADING JOB json_load2 FOR GRAPH companyGraph {
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LOAD "/tmp/data.json"
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TO VERTEX company VALUES($"company":"name":"value",$"company":"name":"value"),
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TO VERTEX members VALUES($"members",$"members") USING JSON_FILE="true", EOL="\03";
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}
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encoding.json
267B
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encoding.json - Download
encoding.json
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{"encoding": "UTF-7","plug-ins":["c"],"indent" : { "length" : 30, "use_space": true }}
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{"encoding":"UTF-1","indent":{"use_space": "dontloadme"}, "plug-ins" : [null, true, false] }
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{"plug-ins":["C","c++"],"indent":{"length" : 3, "use_space": false},"encoding":"UTF-6"}
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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"
0
"UTF-6"
3

Loading Parquet Data

TigerGraph can load data from Parquet files if they are stored in AWS S3 buckets. For more details on how to set up S3 data sources and loading jobs, read the AWS S3 Loader User Guide. In the background TigerGraph uses the JSON loading functionality to read data from Parquet files, so the JSON specific information in the previous section applies.
In order to load Parquet data, you need to:
  1. 1.
    Specify "file.reader.type": "parquet" in the S3 file configuration file or argument
  2. 2.
    Specify JSON_FILE="true" in the USING clause of the LOAD statements
  3. 3.
    Refer to JSON keys (≈ Parquet "column names") instead of column numbers
You will probably want to add USING EOF="true" to your RUN LOADING JOB statement to explicitly indicate to the loading job to stop after consuming all data from the Parquet source, not to expect further entries.
An example of a Parquet loading setup is shown below:
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CREATE DATA_SOURCE S3 s3ds = "{\"file.reader.settings.fs.s3a.access.key\":\"myaccesskey\",\"file.reader.settings.fs.s3a.secret.key\":\"mysecretkey\"}" FOR GRAPH companyGraph
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CREATE LOADING JOB parquet_load FOR GRAPH companyGraph {
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DEFINE FILENAME f = "$s3ds:{\"file.uris\": \"s3://mybucket/mydata.parquet\", \"file.reader.type\": \"parquet\"}";
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LOAD f
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TO VERTEX members VALUES($"members", $"members") USING JSON_FILE="true";
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}
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RUN LOADING JOB parquet_load USING EOF="true"
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VERTEX_MUST_EXIST Parameter

Normally, if vertices do not exist when loading data to edges, a vertex will be created for the connecting edge, using default values for all attributes. Using the VERTEX_MUST_EXIST="true" option will load data only if the vertices on both sides of an edge already exist, therefore no longer creating extra vertices.
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CREATE LOADING JOB load_edge FOR GRAPH MyGraph {
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DEFINE FILENAME f;
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LOAD f
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TO EDGE MyEdge VALUES ($1, $2, $3,) USING VERTEX_MUST_EXIST="true";
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}
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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
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TO TEMP_TABLE table_name (id_name [, attr_name]*) VALUES (id_expr [, attr_expr]*)
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[WHERE conditions] [OPTION (options)]
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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 in 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
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Text
book1.dat
book1.dat
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101|"Harry Potter and the Philosopher's Stone"|"fiction,fantasy,young adult"
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102|"The Three-Body Problem"|"fiction,science fiction,Chinese"
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The following 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.
One-level Flatten Function loading (load_book_flatten1.gsql)
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CREATE LOADING JOB load_books_flatten1 FOR GRAPH Book_rating {
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DEFINE FILENAME f;
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LOAD f
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TO VERTEX Book VALUES ($0, $1, _),
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TO TEMP_TABLE t1(bookcode,genre) VALUES ($0, flatten($2,",",1))
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USING QUOTE="double", SEPARATOR="|";
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LOAD TEMP_TABLE t1
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TO VERTEX Genre VALUES($"genre", $"genre"),
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TO EDGE book_genre VALUES($"bookcode", $"genre");
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}
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RUN LOADING JOB load_books_flatten1 USING f="../data/book1.dat"
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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:
bookcode
genre
101