2.3
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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 which are found in enterprise ETL (Extract,Transform, and Load) systems.

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

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

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

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

  • DEFINE statements

  • LOAD statements, which can have several clauses

New LOADING JOB Capabilities

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

  • The TigerGraph platform can handle concurrent loading jobs, which can greatly increase throughput.

  • The data file locations can be specified at compile time or at run time. Run-time settings override compile-time settings.

  • A loading job definition can include several input files. When running the job, the user can choose to run only part of the job by specifying only some of the input files.

  • Loading jobs can be monitored, aborted, and restarted.

Concurrent Loading

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

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

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

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

  • A loading job begins with CREATE LOADING JOB. (Note that the keyword "LOADING" is included.)

  • A new statement type, DEFINE FILENAME, is added, to define filename variables.

  • The file locations can refer either to the local machine, to specific machines, or to all machines.

  • When a job starts, it is assigned a job_id. Using the job_id, you can check status, abort a job, or restart a job.

Below is a simple example:

Concurrent Loading Job Example
CREATE LOADING JOB job1 FOR GRAPH graph1 {

   DEFINE FILENAME file1 = "/data/v1.csv";
   DEFINE FILENAME file2;

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

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

pre-v2.0 CREATE JOB syntax is deprecated

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

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

Example loading jobs and data files for the book_rating schema defined earlier in the document are available in the /doc/examples/gsql_ref folder in your TigerGraph platform installation.

CREATE LOADING JOB Block

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

Loading type

Block Header

Has DEFINE FILENAME statements?

Run

v2.0 loading

CREATE LOADING JOB

Yes

RUN LOADING JOB

Non-concurrent offline loading (DEPRECATED)

CREATE LOADING JOB

No

RUN JOB

Non-concurrent online loading (DEPRECATED)

CREATE ONLINE_POST JOB

Not permitted

RUN JOB USING FILENAME...

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

CREATE LOADING JOB

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

CREATE LOAD for offline loading
CREATE LOADING JOB job_name FOR GRAPH graph_name {
   [zero or more DEFINE statements;]
   [zero or more LOAD statements;] | [zero or more DELETE statements;]
}

LOAD or DELETE Statements As of version 2.2, a LOADING JOB may contain either LOAD or DELETE statements but not both. A JOB which includes both will be rejected when the CREATE statement is executed.

DROP JOB statement

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

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.

DEFINE FILENAME filevar ["=" filepath_string ]; 
filepath_string = ( path | " all :" path | " any :" path | mach_aliases " :" path ["," mach_aliases ":" path ]* ) 
mach_aliases = name["|"name]*

The filevar is optionally followed by a filepath_string , which tells the job where to find input data. As the name suggests, filepath_string is a string value. Therefore, it should start and end with double quotes.

filepath_string

There are four options for filepath_string :

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

path examples
"/data/graph.csv"

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

An absolute path may begin with the session variable $sys.data_root.

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

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

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

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

  • "all:" path : If the path is prefixed with all: , then the loading job will attempt to run on every machine in the cluster which has a RESTPP component, and each machine will look locally for data at path . I f the path is not valid on any of the machines, the job will be aborted . Also, the session parameter $sys.data_root may not be used.

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

  • "any:" path : If the path is prefixed with any: , then the loading job will attempt to run on every machine in the cluster which has a RESTPP component, and each machine will look locally for data at path . If the path is not valid on any of the machines, those machines are skipped. Also, the session parameter $sys.data_root may not be used.

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

  • A list of machine-specific paths : A machine_alias is a name such as m1, m2, etc. which is defined when the cluster configuration is set. For this option, the filepath_string may include a list of paths, separated by commas. If several machines have the same path, the paths can be grouped together by using a list of machine aliases, with the vertical bar "|" as a separator. The loading job will run on whichever machines are named; each RESTPP-LOADER will work on its local files.

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

DEFINE HEADER

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

DEFINE HEADER header_name = " column_name "[," column_name "]*;

DEFINE INPUT_LINE_FILTER

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

DEFINE INPUT_LINE_FILTER filter_name = boolean_expression_using_column_variables ;

LOAD statements

A LOAD statement tells the GSQL loader how to parse a data line into column values (tokens), and then describes how the values should be used to create a new vertex or edge instance. One LOAD statement can be used to generate multiple vertices or edges, each vertex or edge having its own Destination_Clause , as shown below. Additionally, two or more LOAD statements may refer to the same input data file. In this case, the GSQL loader will merge their operations so that both of their operations are executed in a single pass through the data file.

The LOAD statement has many options. This reference guide provides examples of key features and options. The Platform Knowledge Base / FAQs and the tutorials, such as Get Started with TigerGraph , provide additional solution- and application-oriented examples.

Different LOAD statement types have different rules for the USING clause; see the USING clause section below for specifics.

LOAD statement

LOAD [filepath_string|filevar|TEMP_TABLE table_name ] Destination_Clause [, Destination_Clause ]* [USING clause ];

The filevar must have been previously defined in a DEFINE FILENAME statement.

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

"__GSQL_FILENAME_n__": Position-based File Identifiers

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

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

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

Destination Clause

A Destination_Clause describes how the tokens from a data source should be used to construct one of three types of data objects : a vertex, an edge, or a row in a temporary table (TEMP_TABLE). The destination clause formats for the three types are very similar, but we show them separately for clarity:

Vertex Destination Clause
TO VERTEX vertex_type_name VALUES (id_expr [, attr_expr]*)
    [WHERE conditions] [OPTION (options)]
Edge Destination Clause
TO EDGE edge_type_name VALUES (source_id_expr, target_id_expr [, attr_expr]*)
    [WHERE conditions] [OPTION (options)]
TEMP_TABLE Destination Clause
TO TEMP_TABLE table_name (id_name [, attr_name]*) VALUES (id_expr [, attr_expr]*)
    [WHERE conditions] [OPTION (options)]

For the TO VERTEX and TO EDGE destination clauses, the vertex_type_name or edge_type_name must match the name of a vertex or edge type previously defined in a CREATE VERTEX or CREATE UNDIRECTED|DIRECTED EDGE statement. The values in the VALUE list(id_expr, attr_expr1, attr_expr2,...) are assigned to the id(s) and attributes of a new vertex or edge instance, in the same order in which they are listed in the CREATE statement. id_expr obeys the same attribute rules as attr_expr , except that only attr_expr can use the reducer function, which is introduced later.

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

Attributes and Attribute Expressions

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

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

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

Data file name: $sys.file_name refers to the current input data file.

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

Example: using $sys.file_name in an attribute expression
LOAD "xx/yy/a.csv" TO VERTEX person VALUES ($0, $1, $sys.file_name)

  • The PRIMARY_ID of a person vertex comes from column $0 of the file "xx/yy/a.csv".

  • The next attribute of a person vertex comes from column $1.

  • The next attribute of a person vertex is given the value "xx/y/a.csv" (the filename itself).

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. UINT: Any non-digit character. (Out-of-range values cause overflow instead of rejection)

  2. INT: Any non-digit or non-sign character. (Out-of-range values cause overflow instead of rejection)

  3. FLOAT and DOUBLE: Any wrong format

  4. STRING, STRING COMPRESS, FIXED_BINARY: N/A

  5. DATETIME: Wrong format, invalid date time, or out of range.

  6. Complex type: Depends on the field type or element type. Any invalid field (in UDT), element (in LIST or SET), key or value (in MAP) causes rejection.

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

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

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

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

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

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

  1. If the LOAD is creating a new vertex or edge, then the skipped attribute will be assigned the default value.

  2. If the LOAD is overwriting an existing vertex or edge, then the skipped attribute will retain its existing value.

More Complex Attribute Expressions

An attribute expression may use column tokens (e.g., $0), literals (constant numeric or string values), any of the built-in loader token functions, or a user-defined token function. Attribute expressions may not contain mathematical or boolean operators (such as +, *, AND). The rules for attribute expressions are the same as those for id expressions, but an attribute expression can additionally use a reducer function:

  • id_expr := $column_number | $"column_name" | constant | $sys.file_name | token_function_name( id_expr [, id_expr ]* )

  • attr_expr := id_expr | REDUCE(reducer_function_name(id _expr ))

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

The subsections below describe details about loading particular data types.

Loading a DOUBLE or FLOAT Attribute

A floating point value has the basic format 

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

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

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

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


# Invalid floating point values
-198,256.03
9.99 E-22

Loading a DATETIME Attribute

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

  • %Y-%m-%d %H:%M:%S (e.g., 2011-02-03 01:02:03)

  • %Y/%m/%d %H:%M:%S (e.g., 2011/02/03 01:02:03)

  • %Y-%m-%dT%H:%M:%S.000z (e.g., 2011-02-03T01:02:03.123z, 123 will be ignored)

  • %Y-%m-%d (only date, no time, e.g., 2011-02-03 )

  • %Y/%m/%d (only date, no time, e.g., 2011/02/03)

  • Any integer value (Unix Epoch time, where Jan 1, 1970 at 00:00:00 is integer 0)

Format notation:

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

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

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

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

Loading a User-Defined Type (UDT) Attribute

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

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

Loading a LIST or SET Attribute

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

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

Example: Cumulative loading of multiple rows to a SET/LIST
CREATE VERTEX test_vertex (PRIMARY_ID id STRING, iset SET<INT>, ilist LIST<INT>)
CREATE UNDIRECTED EDGE test_edge(FROM test_vertex, TO test_vertex)
CREATE GRAPH test_set_list (*)

CREATE LOADING JOB load_set_list FOR GRAPH test_set_list {
  DEFINE FILENAME f;
  LOAD f TO VERTEX test_vertex VALUES ($0, $1, $1);                                                                                  
}
RUN LOADING JOB load_set_list USING f="./list_set_vertex.csv"
list_set_vertex.csv
1,10
3,30
1,20
3,30
3,40
1,20

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

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

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

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

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

Example: SET/LIST loading by SPLIT() example
CREATE VERTEX test_vertex (PRIMARY_ID id STRING, ustrset SET<STRING>, ilist LIST<INT>)
CREATE UNDIRECTED EDGE test_edge(FROM test_vertex, TO test_vertex)
CREATE GRAPH test_split (*)

CREATE LOADING JOB set_list_job FOR GRAPH test_split {
  DEFINE FILENAME f;
  LOAD f TO VERTEX test_vertex VALUES ($0, SPLIT($1,"|") , SPLIT($2,"#") );                                                                                  
}
RUN LOADING JOB set_list_job USING f="./split_list_set.csv"
split_list_set.csv
vid,names,numbers 
v1,mike|tom|jack, 1 # 2 # 3 
v2,john, 5 # 4 # 8

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

Loading a MAP Attribute

There are three methods to load a MAP.

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

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

Method 1 : Below is the syntax to load a MAP by the first method: Use an arrow (->) to separate the map's key and value.

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

Method 2 : The second method is to use the MAP() function. If there are multiple key-value pairs among multiple columns, MAP() can load them together. Below is an example:

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

Method 3 : The third method is to use the SPLIT() function. Similar to the SPLIT() in loading LIST or SET, the SPLIT() function can be used when the key-value pair is in one column and separated by a key-value separator, or multiple key-value pairs are in one column and separated by element separators and key-value separators. SPLIT() here has three parameters: The first is the column index, the second is the key-value separator, and the third is the element separator. The third parameter is optional. If one row of raw data only has one key-value pair, the third parameter can be skipped. Below are the examples without and with the given element separator.

example data with one key-value pair per line
vid,key_value
v1,1:mike
v2,2:tom
v1,3:lucy
example data with multiple key-value pairs per line
vid,key_value_list
v1,1:mike#4:lin
v2,2:tom
v1,3:lucy#1:john#6:jack
Loading a MAP by method 3: SPLIT() function
CREATE VERTEX v_map  (PRIMARY_ID id STRING, att_map MAP<INT, STRING>)
CREATE GRAPH test_graph (*)
CREATE LOADING JOB load_map FOR GRAPH test_graph {
    DEFINE FILENAME f;
    LOAD f TO VERTEX v_map  VALUES ($0, SPLIT($1, ":", "#") );
}

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

Loading Wildcard Type Edges

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

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

Built-in Loader Token Functions

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

Token Functions for Attribute Expressions

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

Function name and parameters

Output type

Description of function

gsql_reverse( 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_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_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. The timestamp should be in one of the following 3 formats: "%Y-%m-%d %H:%M:%S" "%Y/%m/%d %H:%M:%S" "%Y-%m-%dT%H:%M:%S.000z" // text after . is ignored

gsql_current_time_epoch(0)

uint

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

flatten( column_to_be_split, group_separator, 1 )

flatten( column_to_be_split, group_separator, sub_field_separator, number_of_sub_fields_in_one_group )

See the section "TEMP_TABLE and Flatten Functions" below.

flatten_json_array ( $"array_name" )

flatten_json_array ( $"array_name", $"sub_obj_1", $"sub_obj_2", ..., $"sub_obj_n" )

See the section "TEMP_TABLE and Flatten Functions" below.

split( column_to_be_split, element_separator )

split( column_to_be_split, key_value_separator, element _separator )

See the section "Loading a LIST or SET Attribute" above.

See the section "Loading a MAP Attribute" above.

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).

Reducer Functions

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

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

REDUCE( reducer_function ( input_expr ) )

where reducer_function is one of the functions in the table below. input_expr can include non-reducer functions, but reducer functions cannot be nested.

Each reducer function is overloaded so that one function can be used for several different data types. For primitive data types, the output type is the same as the input_expr type. For LIST, SET, and MAP containers, the input_expr type is one of the allowed element types for these containers (see "Complex Types" in the Attribute Data Types section). The output is the entire container.

Function name

Data type of arg : Description of function's return value

max( arg )

INT, UINT, FLOAT, DOUBLE: maximum of all arg values cumulatively received

min( arg )

INT, UINT, FLOAT, DOUBLE: minimum of all arg values cumulatively received

add( arg )

INT, UINT, FLOAT, DOUBLE: sum of all arg 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 operation can be used to express complex operation between numeric types. Just as in ordinary mathematical expressions, parentheses can be used to define a group and to modify the order of precedence.

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

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

  • Relational Operators: <, >, ==, !=, <=, >= Comparisons can be performed between two numeric values or between two string values.

  • Predicate Operators:

    • AND, OR, NOT operators are the same as in SQL. They can be used to combine multiple conditions together. E.g., $0 < "abc" AND $1 > "abc" selects the rows with the first token less than "abc" and the second token greater than "abc". E.g., NOT $1 < "abc" selects the rows with the second token greater than or equal to "abc".

    • IS NUMERIC token IS NUMERIC returns true if token is in numeric format. Numeric format include integers, decimal notation, and exponential notation. Specifically, IS NUMERIC is true if token matches the following regular expression: (+/-) ? [0-9] + (.[0-9]) ? [0-9] * ((e/E)(+/-) ? [0-9] +) ? . Any leading space and trailing space is skipped, but no other spaces are allowed. E.g., $0 IS NUMERIC checks whether the first token is in numeric format.

    • IS EMPTY token IS EMPTY returns true if token is an empty string. E.g., $1 IS EMPTY checks whether the second token is empty.

    • IN token IN ( set_of_values ) returns true if token is equal to one member of a set of specified values. The values may be string or numeric types. E.g., $2 IN ("abc", "def", "lhm") tests whether the third token equals one of the three strings in the given set. E.g., to_int($3) IN (10, 1, 12, 13, 19) tests whether the fourth token equals one of the specified five numbers.

    • BETWEEN ... AND token BETWEEN lowerVal AND upperVal returns true if token is within the specified range, inclusive of the endpoints. The values may be string or numeric types. E.g., $4 BETWEEN "abc" AND "def" checks whether the fifth token is greater than or equal to "abc" and also less than or equal to "def" E.g., to_float($5) BETWEEN 1 AND 100.5 checks whether the sixth token is greater than or equal to 1.0 and less than or equal to 100.5.

Token functions in the WHERE clause

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

Function name

Output type

Description of function

to_int( main_string )

int

Converts main_string to an integer value.

to_float( main_string )

float

Converts main_string to a float value.

concat( string1, string2 )

string

Returns a string which is the concatenation of string1 and string2 .

token_len( main_string )

int

Returns the length of main_string.

gsql_is_not_empty_string( main_string )

bool

Returns true if main_string is empty after removing white space. Returns false otherwise.

gsql_token_equal( string1, string2 )

bool

Returns true if string1 is exactly the same (case sensitive) as string2 . Returns false otherwise.

gsql_token_ignore_case_equal( string1, string2 )

bool

Returns true if string1 is exactly the same (case insensitive) as string2 . Returns false otherwise.

gsql_is_true( main_string )

bool

Returns true if main_string is either "t" or "true" (case insensitive). Returns false otherwise.

gsql_is_false( main_string )

bool

Returns true if main_string is either "f" or "false" (case insensitive). Returns false otherwise.

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

User-Defined Token Functions

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

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

1. To test that your function compiles:

./compile

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

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

User-defined Token Functions for Attribute Expressions

Attribute type

Function signature

string or string compress

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

bool

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

uint

extern "C" uint64_t funcName (const char* const iToken[], uint32_t iTokenLen[], uint32_t iTokenNum)

int

extern "C" int64_t funcName (const char* const iToken[], uint32_t iTokenLen[], uint32_t iTokenNum)

float

extern "C" float funcName (const char* const iToken[], uint32_t iTokenLen[], uint32_t iTokenNum)

double

extern "C" double funcName (const char* const iToken[], uint32_t iTokenLen[], uint32_t iTokenNum)

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

If the attribute type is not string nor string compress, the return type should be the corresponding type: bool for bool; uint64_t for uint; int64_t for int; float for float double for double. If the attribute type is string or string compress, the return type should be void, and use the extra parameters ( char *const oToken, uint32_t& oTokenLen) for storing the return string. oToken is the returned string value, and oTokenLen is the length of this string.

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

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

User-defined Token Functions for WHERE Clause

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

In the clause "WHERE conditions ",

  • The only type of user-defined token function allowed are those that return a boolean value.

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

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

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

Other Optional LOAD Clauses

OPTION clause

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

USING clause

A USING clause contains one or more optional parameter value pairs:

USING parameter=value [,parameter=value]*

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

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

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

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 "

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 note below)

whether each line is a json object (see Section "JSON Loader" below for more details)

"true", "false"

Default is "false"

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"

VERTEXMUSTEXIST

(See note below)

specifies whether to require that the endpoint vertices of an edge must exist in order to load a given edge.

"true", "false"

Default is "false"

QUOTE parameter

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

  • If QUOTE is not declared, quotation marks are treated as ordinary characters.

  • If QUOTE is declared, but a string does not contain a matching pair of quotation marks, then the string is treated as if QUOTE is not declared.

  • Only the string inside the first pair of quote (from left to right) marks are loaded. For example QUOTE="double", the string a"b"c"d"e will be loaded as b.

  • There is no escape character in the loader, so the only way to include quotation marks within a string is for the string body to use one type of quote (single or double) and to declare the other type as the string boundary marker.

Loading JSON Data

When the USING option JSON_FILE="true" is used, the loader loads JSON objects instead of tabular data. A JSON object is an unordered set of key/value pairs, where each value may itself be an array or object, leading to nested structures. A colon separates each key from its value, and a comma separates items in a collection. A more complete description of JSON format is available at www.json.org . The JSON loader requires that each input line has exactly one JSON object . Instead of using column values as tokens, the JSON loader uses JSON values as tokens, that is, the second part of each JSON key/value pair. In a GSQL loading job, a JSON field is identified by a dollar sign $ followed by the colon-separated sequence of nested key names to reach the value from the top level. For example, given the JSON object {"abc":{"def": "this_value"}}, the identifier $"abc":"def" is used to access "this_value". T he double quotes are mandatory.

An example is shown below:

USING JSON_FILE test schema and loading job
CREATE VERTEX encoding (PRIMARY_ID id STRING, length FLOAT default 10)
CREATE UNDIRECTED EDGE encoding_edge (FROM encoding, TO encoding)
CREATE GRAPH encoding_graph (*)

CREATE LOADING JOB json_load FOR GRAPH encoding_graph {
  LOAD "encoding.json" TO VERTEX encoding
    VALUES ($"encoding", $"indent":"length") USING JSON_FILE="true";
}
RUN JOB json_load
encoding.json
{"encoding": "UTF-7","plug-ins":["c"],"indent" : { "length" : 30, "use_space": true }}
{"encoding":"UTF-1","indent":{"use_space": "dontloadme"}, "plug-ins" : [null, true, false] }
{"plug-ins":["C","c++"],"indent":{"length" : 3, "use_space": false},"encoding":"UTF-6"}

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

id

attr1

"UTF-7"

30

"UTF-1"

0

"UTF-6"

3

VertexMustExist 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 VERTEXMUSTEXIST="true" option will load data only if the vertices on both sides of an edge already exist, therefore no longer creating extra vertices.

CREATE LOADING JOB load_edge FOR GRAPH MyGraph {
    DEFINE FILENAME f;
    LOAD f
        TO EDGE MyEdge VALUES ($1, $2, $3,) USING VERTEXMUSTEXIST="true";
}

TEMP_TABLE and Flatten Functions

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

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

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

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

One-Level Flatten Function

flatten( column_to_be_split, separator, 1 ) is used to parse a one-level group into individual elements. An example is shown below:

book1.dat
101|"Harry Potter and the Philosopher's Stone"|"fiction,fantasy,young adult"
102|"The Three-Body Problem"|"fiction,science fiction,Chinese"
One-level Flatten Function loading (load_book_flatten1.gsql)
CREATE LOADING JOB load_books_flatten1 FOR GRAPH Book_rating {
  DEFINE FILENAME f;
  LOAD f
      TO VERTEX Book VALUES ($0, $1, _),
      TO TEMP_TABLE t1(bookcode,genre) VALUES ($0, flatten($2,",",1))
      USING QUOTE="double", SEPARATOR="|";

  LOAD TEMP_TABLE t1
      TO VERTEX Genre VALUES($"genre", $"genre"),
      TO EDGE book_genre VALUES($"bookcode", $"genre");
}
RUN LOADING JOB load_books_flatten1 USING f="../data/book1.dat"

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

bookcode

genre

101

fiction

101

fantasy

101

young_adult

102

fiction

102

science_fiction

102

Chinese

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

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

  • Create a Genre vertex for each new value of "genre".

  • Create a book_genre edge from "bookcode" to "genre". In this case, each row of TEMP_TABLE t1 generates one book_genre edge.

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

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

Two-Level Flatten Function

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

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

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

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

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

bookcode

genre_id

101

FIC

fiction

101

FTS

fantasy

101

YA

young adult

102

FIC

fiction

102

SF

science fiction

102

CHN

Chinese

Flatten a JSON Array of Primitive Values

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

flatten_json_array_values loading
CREATE VERTEX encoding (PRIMARY_ID id STRING, length FLOAT default 10)
CREATE UNDIRECTED EDGE encoding_edge (FROM encoding, TO encoding)
CREATE GRAPH encoding_graph (*)

CREATE LOADING JOB json_flatten FOR GRAPH encoding_graph {
  LOAD "encoding2.json" TO TEMP_TABLE t2 (name, length)
    VALUES (flatten_json_array($"plug-ins"), $"indent":"length") USING JSON_FILE ="true";
  LOAD TEMP_TABLE t2
    TO VERTEX encoding VALUES ($"name", $"length");
}
RUN LOADING JOB json_flatten
encoding2.json
{"plug-ins" : ["C", "c++"],"encoding" : "UTF-6","indent" : { "length" : 3, "use_space": false}}

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

id

length

C

3

c++

3

Flatten a JSON Array of JSON Objects

flatten_json_array ( $"array_name", $"sub_obj_1", $"sub_obj_2", ..., $"sub_obj_n" ) parses a JSON array of JSON objects. "array_name" is the name of the array, and the following parameters $"sub_obj_1", $"sub_obj_2", ..., $"sub_obj_n" are the field key names in each object in the array. See complete example below:

encoding3.json
{"encoding":"UTF-1","indent":{"use_space": "dontloadme"}, "plug-ins" : [null, true, false, {"lang":"golang","prop":{"age":"noidea"}}]}
{"encoding": "UTF-8", "plug-ins" : [{"lang": "pascal", "score":"1.0", "prop":{"age":"old"}}, {"lang":"c++", "score":2.0}],"indent":{"length" :12,"use_space": true}}
{"encoding": "UTF-7",  "plug-ins" : [{"lang":"java", "score":2.22}, {"lang":"python", "score":3.0},{"lang":"go", "score":4.0, "prop":{"age":"new"}}],"indent" : { "length" : 30, "use_space": true }}
{"plug-ins" : ["C", "c++"],"encoding" : "UTF-6","indent" : { "length" : 3, "use_space": false}}
json_flatten_array_test.gsql
CREATE VERTEX encoding3 (PRIMARY_ID id STRING, score FLOAT default -1.0, age STRING default "Unknown", length INT default -1)
CREATE UNDIRECTED EDGE encoding3_edge (FROM encoding3, TO encoding3)
CREATE GRAPH encoding_graph (*)

CREATE LOADING JOB json_flatten_array FOR GRAPH encoding_graph {
  LOAD "encoding3.json" TO TEMP_TABLE t3 (name, score, prop_age, indent_length )
    VALUES (flatten_json_array($"plug-ins", $"lang", $"score", $"prop":"age"), $"indent":"length")
    USING JSON_FILE="true";
  LOAD TEMP_TABLE t3
    TO VERTEX encoding3 VALUES ($"name", $"score", $"prop_age", $"indent_length");
}
RUN LOADING JOB json_flatten_array

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

id

score

age

length

"golang"

default

"noidea"

default

"pascal"

1.0

"old"

12

"c++"

2.0

default

12

"java"

2.22

default

30

"python"

3.0

default

30

"go"

4.0

"new"

30

Flatten a JSON Array in a CSV file

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

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

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

json_flatten_cvs.gsql
CREATE VERTEX encoding3 (PRIMARY_ID id STRING, score FLOAT default -1.0, age STRING default "Unknown", length INT default -1)
CREATE UNDIRECTED EDGE encoding3_edge (FROM encoding3, TO encoding3)
CREATE GRAPH encoding_graph (*)

CREATE LOADING JOB json_flatten_cvs FOR GRAPH encoding_graph {
  LOAD "encoding.csv" TO TEMP_TABLE t4 (name, score, prop_age, indent_length )
    VALUES ($0,flatten_json_array($1, $"score", $"prop":"age", $"indent":"length"))
    USING SEPARATOR="|";
  LOAD TEMP_TABLE t4
    TO VERTEX encoding3 VALUES ($"name", $"score", $"prop_age", $"indent_length");
}
RUN LOADING JOB json_flatten_cvs

The above example generates the temporary table shown below:

id

score

age

length

golang

-1 (default)

noidea

-1 (default)

pascal

1

old

-1 (default)

c++

2

unknown (default)

12

java

2.22

new

2

python

3

unknown (default)

4

go

4

new

-1 (default)

flatten_json_array in csv

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

DELETE statement

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

In the v2.0 syntax, there is now a " FROM (filepath_string | filevar) " clause just before the WHERE clause.

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

DELETE VERTEX | EDGE Syntax
CREATE LOADING JOB abc FOR GRAPH graph_name {
  DEFINE FILENAME f;
  # 1. Delete each vertex which has the given vertex type and primary id.
  DELETE VERTEX vertex_type_name (PRIMARY_ID id_expr) FROM f [WHERE condition] ;

  # 2. Delete each edge which has the given edge type, source vertex id, and destination vertex id.
  DELETE EDGE edge_type_name (FROM id_expr, TO id_expr) FROM f [WHERE condition] ;

  # 3. Delete all edges which have the given edge type and source vertex id. (Destination vertex id is left open.)
  DELETE EDGE edge_type_name (FROM id_expr) FROM f [WHERE condition] ;

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

An example using book_rating data is shown below:

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

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

offline2online Job Conversion (DEPRECATED)

offline2online <offline_job_name>

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

Online Job Names

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

CREATE LOADING JOB loadEx FOR GRAPH graphEx {
  LOAD "fileA" TO ...
  LOAD "fileB" TO ...
}

then running the GSQL command offline2online loadEx will create two new online loading jobs, called loadEx_fileA and loadEx_fileB . The converted loading jobs are installed in the GSQL system; they are not available as text files. However, if there are already jobs with these names, then a version number will be appended: first "_1", then "_2", etc.

For example, if you were to execute offline2online loadEx three times, this would generate the following online jobs:

  • 1st time: loadEx_fileA, loadEx_fileB

  • 2nd time: loadEx_fileA_1, loadEx_fileB_1

  • 3rd time: loadEx_fileA_2, loadEx_fileB_2

Conversion and RUN JOB Details

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

  • input data filename

  • SEPARATOR

  • HEADER

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

When running any online loading job, the input data filename and the separator character must be provided. See sections on the USING clause and Running a Loading Job for more details.

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

The following example is taken from the Social Network case in the GSQL Tutorial with Real-Life Examples . In version 0.2 of the tutorial, we used offline loading. The job below uses the same syntax as v0.2, but some names have been updated:

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

  LOAD "data/social_connection.csv"
    TO EDGE SocialConn VALUES ($0, $1)
    USING SEPARATOR=",", HEADER="false";
}

To run, this job:

RUN LOADING JOB load_social

Note that the first LOAD statement has HEADER="true", but is does not make use of column names. It simply uses column indices $0, $1, $2, and $3. Therefore, the HEADER option can still be used with the converted job. Running offline2online load_social1 , creates two new jobs called load_social_social_users.csv and load_social_social_connection.csv.

The equivalent run commands for the jobs are the following:

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

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

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

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