Load from Spark Dataframe

The TigerGraph Spark Connector employs Apache Spark to read data from a Spark DataFrame (or Data Lake) and write to TigerGraph. This connector has multiple optimizations for high performance, scalability, and management.

Compatibility

  • TigerGraph 3.6.0 or higher. Job level loading statistics are only available for v3.10+.

  • Spark 3.2 or higher with Scala 2.12 and Scala 2.13.

  • JAVA 8 or higher.

Users can leverage it to connect TigerGraph to the Spark ecosystem and load data from any Spark-compatible data sources, such as:

The legacy Spark Connection Via JDBC Driver is deprecated. Please migrate to this new connector.

Overview

The first step is to read data into a Spark dataframe. Then, using a TigerGraph loading job which maps data fields from the dataframe into graph elements, the connector pulls data from Spark into TigerGraph.

Spark dataframe example:
+--------------------+-------------+-------------+-----------+--------------------+------+
|        creationDate|           id|   locationIP|browserUsed|             content|length|
+--------------------+-------------+-------------+-----------+--------------------+------+
|2012-07-04T06:10:...|7696585588755| 46.23.82.182|    Firefox|                 LOL|     3|
|2012-08-22T17:22:...|8246341402699|  27.62.125.4|     Chrome|              roflol|     6|
|2012-05-08T21:02:...|7146829775042|  61.1.50.205|     Chrome|              roflol|     6|
|2012-11-22T01:25:...|9345853030654|190.95.68.192|    Firefox|About Sergei Eise...|    79|
|2012-11-11T08:59:...|9345853030710|166.75.225.76|     Chrome|                good|     4|
+--------------------+-------------+-------------+-----------+--------------------+------+
The connector concatenates columns to delimited data:
    - loading.separator = "|"
    - loading.eol = "\n"
the processed data would be:
    2012-07-04T06:10:43.489+00:00|7696585588755|46.23.82.182|Firefox|LOL|3
    2012-08-22T17:22:20.315+00:00|8246341402699|27.62.125.4|Chrome|roflol|6
    2012-05-08T21:02:39.145+00:00|7146829775042|61.1.50.205|Chrome|roflol|6
    2012-11-22T01:25:39.670+00:00|9345853030654|190.95.68.192|Firefox|About Sergei Eisenstein, pioneering SAbout Steven Spielberg, makers in thAbout|79
    2012-11-11T08:59:21.311+00:00|9345853030710|166.75.225.76|Chrome|good|4
The processed data will be sent to TigerGraph batch by batch:
    - loading.job = "load_Comment"
    - loading.filename = "file_Comment"

To let TigerGraph parse the data chunk correctly, please make sure you are setting loading.separator and loading.eol to characters which do not appear in your data fields.

Setup

Download the JARs

This connector can be downloaded from the Maven central repository: Maven Central. The connector is available in 3 release formats:

  • tigergraph-spark-connector-<version>.jar: The JAR file containing only the compiled classes of the connector, which does not include any dependencies.

  • tigergraph-spark-connector-<version>-jar-with-dependencies.jar: The JAR file that includes compiled classes, as well as all the dependencies.

  • tigergraph-spark-connector-<version>.tar.gz: The compressed TAR archive that includes tigergraph-spark-connector-<version>.jar and dependencies in separate JAR files.

To use the TigerGraph Spark connector in a Spark shell, use the --jars option:

spark-shell --jars tigergraph-spark-connector-<version>-jar-with-dependencies.jar

If you want to include the TigerGraph Spark Connector in your Spark installation, add the JAR with dependencies to Spark’s jars folder.

Create a Loading Job

This loading job should map the dataframe’s columns to the desired graph element attributes.

Example of simple graph schema and loading job
gsql '
    CREATE VERTEX Comment (PRIMARY_ID id UINT, creationDate DATETIME, locationIP STRING, browserUsed STRING, content STRING, length UINT) WITH primary_id_as_attribute="TRUE", STATS="outdegree_by_edgetype"
    CREATE GRAPH demo_graph (*)
'

gsql -g demo_graph '
    CREATE LOADING JOB load_Comment FOR GRAPH test_graph {
        DEFINE FILENAME file_Comment;
        LOAD file_Comment
            TO VERTEX Comment VALUES ($1, $0, $2, $3, $4, $5) USING header="true", separator="|";
 }
'

Configure the Connector

If you are using Spark Connector 0.2.0+, the following configuration parameters are required: version, log.level,log.path.
Key Default Value Description Group

url

(none)

The connection URL to TigerGraph cluster. It can be a list of URLs separated by comma for load balancing.

Example: http://192.168.1.1:14240, http://192.168.1.2:14240, http://192.168.1.3:14240

General

graph

(none)

The graph name.

username

(none)

The GSQL username.

Authentication

(You can choose any authentication method for data loading, but it’s recommended to give username/password pair, which can generate and refresh token automatically.)

password

(none)

The GSQL password.

Authentication

secret

(none)

The GSQL secret.

Authentication

token

(none)

The Bearer token for RESTPP.

Authentication

loading.job

(none)

The GSQL loading job name.

Loading Job

loading.filename

(none)

The filename defined in the loading job.

Loading Job

loading.separator

,

The column separator.

Loading Job

loading.eol

\n

The line separator.

Loading Job

loading.batch.size.bytes

2097152

The maximum batch size in bytes.

Loading Job

loading.timeout.ms

(none)

The loading timeout per batch.

Loading Job

loading.max.percent.error

(none)

The threshold of the error objects count. The loading job will be aborted when reaching the limit. Only available for TigerGraph version 3.10.0+.

Loading Job

loading.max.num.error

(none)

The threshold of the error objects percentage. The loading job will be aborted when reaching the limit. Only available for TigerGraph version 3.10.0+.

Loading Job

loading.retry.interval.ms

5000

The initial retry interval for transient server errors.

Loading Job

loading.max.retry.attempts

10

The maximum retry attempts for transient server errors.

Loading Job

loading.max.retry.interval.ms

30000

The maximum retry interval for transient server errors.

Loading Job

ssl.mode

basic

The SSL mode: basic, verifyCA and verifyHostname.

When setting it to verifyCA and verifyHostname, the truststore file should be given.

SSL

ssl.truststore

(none)

Filename of the truststore which stores the SSL certificate chains.

Add --files /path/to/trust.jks when submitting the Spark job.

SSL

ssl.truststore.type

JKS

Truststore type, e.g., JKS, PKCS12

SSL

ssl.truststore.password

(none)

Password of the truststore.

SSL

io.connect.timeout.ms

30000

Connect timeout in ms.

Transport Timeout

io.read.timeout.ms

60000

Socket read timeout in ms.

Transport Timeout

io.retry.interval.ms

5000

The initial retry interval for transport timeout.

Transport Timeout

io.max.retry.interval.ms

10000

The maximum retry interval for transport timeout.

Transport Timeout

io.max.retry.attempts

5

The maximum retry attempts for transport timeout.

Transport Timeout

oauth2.url

(none)

The URL where the Client Credentials grant request should be sent for retrieving the access token. This URL is typically provided by the Identity Provider (IdP) such as Azure AD or Auth0.

For Azure AD: https://login.microsoftonline.com/{tenant_id}/oauth2/v2.0/token

For Auth0: https://{yourDomain}/oauth/token

Note: Use the gadmin command to set the Security.JWT.RSA.PublicKey in advance for JWT verification.

Authentication

oauth2.parameters

(none)

A stringified JSON that carries parameters for retrieving the access token. The required parameters vary depending on the IdP.

For Azure AD: { "client_id": "{client_id}", "client_secret": "{client_secret}", "scope": "https://storage.azure.com/.default" }

For Auth0: { "client_id": "{client_id}", "client_secret": "{client_secret}", "audience": "{api_identifier}" }

Authentication

Configure Logging

It’s highly recommended to set log level to info to monitor the loading status. Configure the Spark log4j conf/log4j2.properties as follows, or other SLF4j bindings if any:

logger.tg.name = com.tigergraph
logger.tg.level = info

Use Case Examples

Some Helpful tips

  • High Availabilty

To ensure fault tolerance, provide a list of all node URLs in the url option for the connector:

"url" -> "https://m1:14240,https://m2:14240,https://m3:14240,https://m4:14240",

Authentication Methods

OAuth2 authentication for Spark was originally added in 4.1.3 and backported to 3.10.4. So, in version 3.10.4, TigerGraph’s Spark Connector supports OAuth2 authentication, enabling it to request JWT tokens from third-party Identity Providers (IdPs) like Azure AD and Auth0, enhancing security and token management for Spark jobs.

We recommend using OAuth2 for authentication. Our implementation supports automatic token requests and refresh, and is more secure that username/password, offering convenience and security for both batch and streaming jobs.

The Spark connector now supports the following authentication methods:

  • oauth2: Recommended for both batch and streaming jobs. The token is automatically requested and refreshed using oauth2.url and oauth2.parameters.

  • token: Suitable for one-time Spark jobs, but lacks support for refresh tokens.

  • username/password: Works for both batch and streaming jobs, but is considered an older, less secure method. The token is automatically requested and refreshed.

  • secret: Also works for batch and streaming jobs, and the token is managed using the GSQL secret, but is less secure compared to OAuth2.

Batch Write Mode

Create the variables:
val GRAPH = "demo_graph"
val URL = "http(s)://hostname:port[,http(s)://hostname:port]*"
val USERNAME = "tigergraph"
val PASSWORD = "tigergraph"
val LOADING_JOB = "load_Comment"
val FILENAME = "file_Comment"
val SEPARATOR = "|"
val VERSION = "3.10.1"
val LOG_LEVEL = "2"
val LOG_PATH = "/tmp/tigergraph-spark-connector.log"
Read data from any Spark data sources into the dataframe:
val df = spark.read.json("path/to/person.json")
Batch write the data into TigerGraph:
df.write
    .format("tigergraph")
    .mode("append")
    .options(
        Map(
            "graph" -> GRAPH,
            "url" -> URL,
            "username" -> USERNAME,
            "password" -> PASSWORD,
            "loading.job" -> LOADING_JOB,
            "loading.filename" -> FILENAME,
            "loading.separator" -> SEPARATOR,
            "version" -> VERSION,
            "log.level" -> LOG_LEVEL,
            "log.path" -> LOG_PATH
        )
    )
    .save()

Streaming Mode with Spark Structured Streaming API

Create the variables:
val GRAPH = "Social_Net"
val URL = "http(s)://hostname:port"
val USERNAME = "tigergraph"
val PASSWORD = "tigergraph"
val LOADING_JOB = "load_person"
val FILENAME = "f1"
val SEPARATOR = "|"
val VERSION = "3.10.1"
val LOG_LEVEL = "2"
val LOG_PATH = "/tmp/tigergraph-spark-connector.log"
Read data from any Spark streaming data sources into the dataframe:
val df = spark.readStream
    .format("kafka")
    .option("subscribe", "person")
    .load()
    .selectExpr("CAST(value AS STRING)").as[(String)]
Streaming write data to TigerGraph:
df.writeStream
    .outputMode("append")
    .format("tigergraph")
    .option("checkpointLocation", "/path/to/checkpoint")
    .options(
        Map(
            "graph" -> GRAPH,
            "url" -> URL,
            "username" -> USERNAME,
            "password" -> PASSWORD,
            "loading.job" -> LOADING_JOB,
            "loading.filename" -> FILENAME,
            "loading.separator" -> SEPARATOR,
            "version" -> VERSION,
            "log.level" -> LOG_LEVEL,
            "log.path" -> LOG_PATH
        )
    )
    .start()
    .awaitTermination()

Load Data from Delta Lake

Batch Write

Load delta table to Spark dataframe:
val df = spark.read.format("delta")
    .load("/path/to/delta/table")
    .select(
        "creationDate",
        "id",
        "locationIP",
        "browserUsed",
        "content",
        "length"
    )
Batch write the data into TigerGraph:
df.write
    .format("tigergraph")
    .mode("append")
    .options(
        Map(
            "graph" -> GRAPH,
            "url" -> URL,
            "username" -> USERNAME,
            "password" -> PASSWORD,
            "loading.job" -> LOADING_JOB,
            "loading.filename" -> FILENAME,
            "loading.separator" -> SEPARATOR,
            "version" -> VERSION,
            "log.level" -> LOG_LEVEL,
            "log.path" -> LOG_PATH
        )
    )
    .save()

Streaming Write(CDC)

Streaming read from delta table:
val df = spark.readStream
    .format("delta")
    .option("readChangeFeed", "true")
    .load("/path/to/delta/table")
    .filter(
        $"_change_type" === "insert" || $"_change_type" === "update_postimage"
    )
    .select(
        "creationDate",
        "id",
        "locationIP",
        "browserUsed",
        "content",
        "length"
    )
Streaming write data to TigerGraph:
df.writeStream
    .outputMode("append")
    .format("tigergraph")
    .option("checkpointLocation", "/path/to/checkpoint")
    .options(
        Map(
            "graph" -> GRAPH,
            "url" -> URL,
            "username" -> USERNAME,
            "password" -> PASSWORD,
            "loading.job" -> LOADING_JOB,
            "loading.filename" -> FILENAME,
            "loading.separator" -> SEPARATOR,
            "version" -> VERSION,
            "log.level" -> LOG_LEVEL,
            "log.path" -> LOG_PATH
        )
    )
    .start()
    .awaitTermination()

For more details on Delta Lake see Welcome to the Delta Lake documentation — Delta Lake Documentation.

Load Data from Iceberg

Batch Write

Load Iceberg table to Spark dataframe:
val df = spark.table("catalog.db.table")
    .select(
        "creationDate",
        "id",
        "locationIP",
        "browserUsed",
        "content",
        "length"
    )
Batch write the data into TigerGraph:
df.write
    .format("tigergraph")
    .mode("append")
    .options(
        Map(
            "graph" -> GRAPH,
            "url" -> URL,
            "username" -> USERNAME,
            "password" -> PASSWORD,
            "loading.job" -> LOADING_JOB,
            "loading.filename" -> FILENAME,
            "loading.separator" -> SEPARATOR,
            "version" -> VERSION,
            "log.level" -> LOG_LEVEL,
            "log.path" -> LOG_PATH
        )
    )
    .save()

Streaming Write(CDC)

Streaming read from Iceberg table:
val df = spark.readStream
    .format("iceberg")
    .option("stream-from-timestamp", 0L)
    .load("catalog.db.table")
    .select(
        "creationDate",
        "id",
        "locationIP",
        "browserUsed",
        "content",
        "length"
    )
Streaming write data to TigerGraph:
df.writeStream
    .outputMode("append")
    .format("tigergraph")
    .option("checkpointLocation", "/path/to/checkpoint")
    .options(
        Map(
            "graph" -> GRAPH,
            "url" -> URL,
            "username" -> USERNAME,
            "password" -> PASSWORD,
            "loading.job" -> LOADING_JOB,
            "loading.filename" -> FILENAME,
            "loading.separator" -> SEPARATOR,
            "version" -> VERSION,
            "log.level" -> LOG_LEVEL,
            "log.path" -> LOG_PATH
        )
    )
    .start()
    .awaitTermination()

For more details on Iceberg see Iceberg Apache: Getting Started

Load Data from Hudi

Batch Write

Load Hudi table to Spark dataframe:
val df = spark.read
    .format("hudi")
    .load("/path/to/hudi/table")
    .select(
        "creationDate",
        "id",
        "locationIP",
        "browserUsed",
        "content",
        "length"
    )
Batch write the data into TigerGraph
df.write
    .format("tigergraph")
    .mode("append")
    .options(
        Map(
            "graph" -> GRAPH,
            "url" -> URL,
            "username" -> USERNAME,
            "password" -> PASSWORD,
            "loading.job" -> LOADING_JOB,
            "loading.filename" -> FILENAME,
            "loading.separator" -> SEPARATOR,
            "version" -> VERSION,
            "log.level" -> LOG_LEVEL,
            "log.path" -> LOG_PATH
        )
    )
    .save()

Streaming Write(CDC)

Streaming read from Hudi table:
val df = spark.readStream
    .format("hudi")
    .load("/path/to/hudi/table")
    .select(
        "creationDate",
        "id",
        "locationIP",
        "browserUsed",
        "content",
        "length"
    )
Streaming write data to TigerGraph:
df.writeStream
    .outputMode("append")
    .format("tigergraph")
    .option("checkpointLocation", "/path/to/checkpoint")
    .options(
        Map(
            "graph" -> GRAPH,
            "url" -> URL,
            "username" -> USERNAME,
            "password" -> PASSWORD,
            "loading.job" -> LOADING_JOB,
            "loading.filename" -> FILENAME,
            "loading.separator" -> SEPARATOR,
            "version" -> VERSION,
            "log.level" -> LOG_LEVEL,
            "log.path" -> LOG_PATH
        )
    )
    .start()
    .awaitTermination()

For more details on Hudi see Spark Guide | Apache Hudi.

Loading Statistics

When you configure the logging properly and set log level to info, the loading statistics will be logged.

There are 3 levels of stats:

  • Batch level: data will be loaded to TigerGraph in micro batches. The counts of malformed or invalid data of the batch will be logged.

  • Partition level: the data source can contain multiple partitions, and the log will show how many rows of the partition have been sent to TigerGraph.

  • Job Level (available for TigerGraph 3.10+): The overall loading statistics of the Spark job aggregated by TigerGraph service KAFKASTRM-LL. This requires providing username and password to the query/gsqlserver endpoint.

Sample loading statistics:
24/01/22 16:15:45 INFO TigerGraphBatchWrite: Overall loading statistics: [ {
    "overall" : {
        "duration" : 15792,
        "size" : 48675207,
        "progress" : 0,
        "startTime" : 1706770863875,
        "averageSpeed" : 29546,
        "id" : "test_graph.load_Comment.spark.all.1706770859889",
        "endTime" : 1706770879667,
        "currentSpeed" : 29546,
        "statistics" : {
            "fileLevel" : {
                "validLine" : 466594,
                "notEnoughToken" : 0,
                "tokenExceedsBuffer" : 0,
                "rejectLine" : 0
            },
            "objectLevel" : {
                "vertex" : [ {
                "validObject" : 466593,
                "typeName" : "Comment",
                "invalidPrimaryId" : 1
                } ]
            }
        }
    },
    "workers" : [ {
        "tasks" : [ {
            "filename" : "file_Comment"
        } ]
    }, {
    "tasks" : [ {
        "filename" : "file_Comment"
        } ]
    } ]
} ]

Row Level Statistics

Row Level Statistics Description

validLine

Number of valid raw data lines parsed.

rejectLine

Number of raw data lines rejected by the reject line rule in the loading script.

notEnoughToken

Number of raw data lines with fewer tokens than what was specified by the loading script.

badCharacter

Number of raw data lines containing invalid characters.

tokenExceedsBuffer

Number of raw data lines containing oversize tokens (see gadmin config get GSQL.OutputTokenBufferSize).

emptyLine

Number of raw data lines that are empty.

Object Level Statistics

Object Level Statistics Description

validObject

Number of data records created.

passedCondition

Number of token lists which passed the WHERE predicate filter.

failedCondition

Number of token lists which failed the WHERE predicate filter.

invalidPrimaryId

Number of token lists where the id token is invalid.

noIdFound

Number of token lists where the id token is empty.

invalidAttribute

Number of token lists where at least one of the attribute tokens is invalid.

incorrectFixedBinaryLength

Number of token lists where at least one of the tokens corresponding to a UDT type attribute is invalid.

invalidVertexType

Number of token lists where at least one of the tokens corresponding to an edge type’s source/target vertex type is invalid.

tokenBankException

Counts how many times a user-defined token function has thrown an exception during data loading.