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Configurations

This page contains references for all the application configurations for Dagger.

Table of Contents#

Generic#

All the sink types of Dagger require the following variables to be set:

STREAMS#

Dagger can run on multiple streams, so STREAMS config can con consist of multiple streams. Multiple streams could be given in a comma-separated format.

For each stream, these following variables need to be configured:

SOURCE_DETAILS#

Defines the type of source to be used as well as its boundedness. This is an ordered JSON array, with each JSON structure containing two fields: SOURCE_NAME and SOURCE_TYPE. As of the latest release, only one source can be configured per stream and hence arity of this array cannot be more than one.

JSON Field NameField Name DescriptionData TypeData Type Description
SOURCE_TYPEDefines the boundedness of the sourceENUM [BOUNDED, UNBOUNDED]
  • BOUNDED is a data source type which is known to be finite and has a fixed start and end point. Once the dagger job is created and running, new additions of data to this source will not be processed.
  • UNBOUNDED is a data source with a fixed starting point but theoretically infinite end point. New data added will be processed even after dagger has been started and is running.
SOURCE_NAMEDefines the formal, registered name of the source in DaggerENUM[KAFKA_SOURCE, PARQUET_SOURCE, KAFKA_CONSUMER]
  • KAFKA_SOURCE is an UNBOUNDED data source type using Apache Kafka as the source.
  • PARQUET_SOURCE is a BOUNDED data source type using Parquet Files present in GCS Buckets as the source.
  • KAFKA_CONSUMER is a BOUNDED source type built on deprecated FlinkKafkaConsumer.
  • Example value: [{"SOURCE_TYPE": "UNBOUNDED","SOURCE_NAME": "KAFKA_CONSUMER"}]
  • Type: required
SOURCE_KAFKA_TOPIC_NAMES#

Defines the list of Kafka topics to consume from. To consume from multiple topics, you need to add | as separator for each topic.

  • Example value: test-topic1|test-topic2
  • Type: required only when KAFKA_CONSUMER or KAFKA_SOURCE is configured in SOURCE_DETAILS
INPUT_SCHEMA_TABLE#

Defines the table name for the stream. FLINK_SQL_QUERY will get executed on this table name.

  • Example value: data_stream
  • Type: required
INPUT_SCHEMA_PROTO_CLASS#

Defines the schema proto class of input data.

  • Example value: com.tests.TestMessage
  • Type: required
INPUT_SCHEMA_EVENT_TIMESTAMP_FIELD_INDEX#

Defines the field index of event timestamp from the input proto class that will be used. The field index value can be obtained from .proto file.

  • Example value: 41
  • Type: required
SOURCE_KAFKA_CONFIG_BOOTSTRAP_SERVERS#

Defines the bootstrap server of Kafka brokers to consume from. Multiple Kafka brokers could be given in a comma-separated format.

  • Example value: localhost:9092
  • Type: required only when KAFKA_CONSUMER or KAFKA_SOURCE is configured in SOURCE_DETAILS
SOURCE_KAFKA_CONSUMER_CONFIG_SECURITY_PROTOCOL#

Defines the security protocol used to communicate with ACL enabled kafka. Dagger supported values are: SASL_PLAINTEXT, SASL_SSL. Find more details on this config here

  • Example value: SASL_PLAINTEXT
  • Type: optional required only for ACL enabled KAFKA_CONSUMER or KAFKA_SOURCE
SOURCE_KAFKA_CONSUMER_CONFIG_SASL_MECHANISM#

Defines the Simple Authentication and Security Layer (SASL) mechanism used for kafka consumer connections with ACL enabled kafka. Dagger supported values are: PLAIN, SCRAM-SHA-256, SCRAM-SHA-512. Find more details on this config here

  • Example value: SCRAM-SHA-512
  • Type: optional required only for ACL enabled KAFKA_CONSUMER or KAFKA_SOURCE
SOURCE_KAFKA_CONSUMER_CONFIG_SASL_JAAS_CONFIG#

Defines the SASL Java Authentication and Authorization Service (JAAS) Config used for JAAS login context parameters for SASL connections in the format used by JAAS configuration files. Find more details on this config here.

  • Example value: org.apache.kafka.common.security.scram.ScramLoginModule required username="admin" password="admin";
  • Type: optional required only for ACL enabled KAFKA_CONSUMER or KAFKA_SOURCE if static JAAS configuration system property java.security.auth.login.config is not configured in flink cluster.
SOURCE_KAFKA_CONSUMER_CONFIG_SSL_PROTOCOL#

Defines the security protocol used to communicate with SSL enabled kafka. Find more details on this config here Dagger supported values are: TLSv1.2, TLSv1.3, TLS, TLSv1.1, SSL, SSLv2 and SSLv3

  • Example value 1: SSL

  • Type: optional required only for SSL enabled KAFKA_CONSUMER or KAFKA_SOURCE

  • Example value 2: TLS

  • Type: optional required only for SSL enabled KAFKA_CONSUMER or KAFKA_SOURCE

SOURCE_KAFKA_CONSUMER_CONFIG_SSL_KEY_PASSWORD#

Defines the SSL Key Password for Kafka source. Find more details on this config here

  • Example value: myKeyPass
  • Type: optional required only for SSL enabled KAFKA_CONSUMER or KAFKA_SOURCE
SOURCE_KAFKA_CONSUMER_CONFIG_SSL_KEYSTORE_LOCATION#

Defines the SSL KeyStore location or path for Kafka source. Find more details on this config here

  • Example value: myKeyStore.jks
  • Type: optional required only for SSL enabled KAFKA_CONSUMER or KAFKA_SOURCE
SOURCE_KAFKA_CONSUMER_CONFIG_SSL_KEYSTORE_PASSWORD#

Defines the SSL KeyStore password for Kafka source. Find more details on this config here

  • Example value: myKeyStorePass
  • Type: optional required only for SSL enabled KAFKA_CONSUMER or KAFKA_SOURCE
SOURCE_KAFKA_CONSUMER_CONFIG_SSL_KEYSTORE_TYPE#

Defines the SSL KeyStore Type like JKS, PKCS12 etc for Kafka source. Find more details on this config here Dagger supported values are: JKS, PKCS12, PEM

  • Example value: JKS
  • Type: optional required only for SSL enabled KAFKA_CONSUMER or KAFKA_SOURCE
SOURCE_KAFKA_CONSUMER_CONFIG_SSL_TRUSTSTORE_LOCATION#

Defines the SSL TrustStore location or path for Kafka source. Find more details on this config here

  • Example value: myTrustStore.jks
  • Type: optional required only for SSL enabled KAFKA_CONSUMER or KAFKA_SOURCE
SOURCE_KAFKA_CONSUMER_CONFIG_SSL_TRUSTSTORE_PASSWORD#

Defines the SSL TrustStore password for Kafka source. Find more details on this config here

  • Example value: myTrustStorePass
  • Type: optional required only for SSL enabled KAFKA_CONSUMER or KAFKA_SOURCE
SOURCE_KAFKA_CONSUMER_CONFIG_SSL_TRUSTSTORE_TYPE#

Defines the SSL TrustStore Type like JKS, PKCS12 for Kafka source. Find more details on this config here Dagger supported values are: JKS, PKCS12, PEM

  • Example value: JKS
  • Type: optional required only for SSL enabled KAFKA_CONSUMER or KAFKA_SOURCE
SOURCE_KAFKA_CONFIG_AUTO_COMMIT_ENABLE#

Enable/Disable Kafka consumer auto-commit. Find more details on this config here.

  • Example value: false
  • Type: optional
  • Default value: false
SOURCE_KAFKA_CONFIG_AUTO_OFFSET_RESET#

Defines the Kafka consumer offset reset policy. Find more details on this config here.

  • Example value: latest
  • Type: optional
  • Default value: latest
SOURCE_KAFKA_CONFIG_GROUP_ID#

Defines the Kafka consumer group ID for Dagger deployment. Find more details on this config here.

  • Example value: dummy-consumer-group
  • Type: required only when KAFKA_CONSUMER or KAFKA_SOURCE is configured in SOURCE_DETAILS
SOURCE_KAFKA_NAME#

Defines a name for the Kafka cluster. It's a logical way to name your Kafka clusters.This helps with identifying different kafka cluster the job might be interacting with.

  • Example value: local-kafka-stream
  • Type: required only when KAFKA_CONSUMER or KAFKA_SOURCE is configured in SOURCE_DETAILS
SOURCE_PARQUET_FILE_PATHS#

Defines the array of date partitioned or hour partitioned file path URLs to be processed by Parquet Source. These can be either local file paths such as /Users/dummy_user/booking_log/dt=2022-01-23/ or GCS file path URLs.

  • Example value: ["gs://my-sample-bucket/booking-log/dt=2022-01-23/", "gs://my-sample-bucket/booking-log/dt=2021-01-23/"]
  • Type: required only when PARQUET_SOURCE is configured in SOURCE_DETAILS

Note:

  1. Each file path in the array can be either a fully qualified file path, for example gs://my-sample-bucket/booking-log/dt=2021-01-23/my_file.parquet or it can be a directory path, for example gs://my-sample-bucket/booking-log/. For the latter, Dagger upon starting will first do a recursive search for all files under the booking-log directory. If SOURCE_PARQUET_FILE_DATE_RANGE is configured, it will only add those files as defined by the range into its internal index for processing and skip the others. If not configured, all the discovered files are processed.
SOURCE_PARQUET_READ_ORDER_STRATEGY#

Defines the ordering in which files discovered from SOURCE_PARQUET_FILE_PATHS will be processed. Currently, this takes just one possible value: EARLIEST_TIME_URL_FIRST, however more strategies can be added later. In EARLIEST_TIME_URL_FIRST strategy, Dagger will extract chronological information from the GCS file path URLs and then begin to process them in the order of ascending timestamps.

  • Example value: EARLIEST_TIME_URL_FIRST
  • Type: optional
  • Default value: EARLIEST_TIME_URL_FIRST
SOURCE_PARQUET_FILE_DATE_RANGE#

Defines the time range which, if present, will be used to decide which files to add for processing post discovery from SOURCE_PARQUET_FILE_PATHS. Each time range consists of two ISO format timestamps, start time and end time, separated by a comma. Multiple time range intervals can also be provided separated by semicolon.

  • Example value: 2022-05-08T00:00:00Z,2022-05-08T23:59:59Z;2022-05-10T00:00:00Z,2022-05-10T23:59:59Z
  • Type: optional

Please follow these guidelines when setting this configuration:

  1. Both the start and end time range are inclusive. For example,
    1. For an hour partitioned GCS folder structure,2022-05-08T00:00:00Z,2022-05-08T10:00:00Z will imply all files of hour 00, 01,..., 10 will be processed.
    2. For a date partitioned GCS folder structure, 2022-05-08T00:00:00Z,2022-05-10T23:59:59Z will imply all files of 8th, 9th and 10th May will be processed.
  2. For a date partitioned GCS folder, it is only the date component that is taken into consideration for selecting which files to process and the time component is ignored. However, for start-time, ensure that the time component is always set to 00:00:00. Otherwise, results might be unexpected and there will be data drop. For example, for a date partitioned GCS bucket folder,
    1. 2022-05-08T00:00:00Z,2022-05-08T10:00:00Z is a valid config. All files for 8th May will be processed.
    2. 2022-05-08T00:00:01Z,2022-05-08T10:00:00Z is not a valid config and will cause the entire data for 2022-05-08 to be skipped.
Sample STREAMS Configuration using KAFKA_CONSUMER as the data source :#
STREAMS = [ {   "SOURCE_KAFKA_TOPIC_NAMES": "test-topic",   "INPUT_SCHEMA_TABLE": "data_stream",   "INPUT_SCHEMA_PROTO_CLASS": "com.tests.TestMessage",   "INPUT_SCHEMA_EVENT_TIMESTAMP_FIELD_INDEX": "41",   "SOURCE_KAFKA_CONSUMER_CONFIG_BOOTSTRAP_SERVERS": "localhost:9092",   "SOURCE_KAFKA_CONSUMER_CONFIG_AUTO_COMMIT_ENABLE": "false",   "SOURCE_KAFKA_CONSUMER_CONFIG_AUTO_OFFSET_RESET": "latest",   "SOURCE_KAFKA_CONSUMER_CONFIG_GROUP_ID": "dummy-consumer-group",   "SOURCE_KAFKA_NAME": "local-kafka-stream",   "SOURCE_DETAILS": [     {       "SOURCE_TYPE": "UNBOUNDED",       "SOURCE_NAME": "KAFKA_CONSUMER"     }   ] }]
Sample STREAMS Configuration using PARQUET_SOURCE as the data source :#
STREAMS = [ {   "INPUT_SCHEMA_TABLE": "data_stream",   "INPUT_SCHEMA_PROTO_CLASS": "com.tests.TestMessage",   "INPUT_SCHEMA_EVENT_TIMESTAMP_FIELD_INDEX": "41",   "SOURCE_PARQUET_FILE_PATHS": [   "gs://p-godata-id-mainstream-bedrock/carbon-offset-transaction-log/dt=2022-02-05/",   "gs://p-godata-id-mainstream-bedrock/carbon-offset-transaction-log/dt=2022-02-03/"   ],   "SOURCE_PARQUET_FILE_DATE_RANGE":"2022-02-05T00:00:00Z,2022-02-05T10:59:59Z;2022-02-03T00:00:00Z,2022-02-03T20:59:59Z"   "SOURCE_PARQUET_READ_ORDER_STRATEGY": "EARLIEST_TIME_URL_FIRST",   "SOURCE_DETAILS": [     {       "SOURCE_TYPE": "BOUNDED",       "SOURCE_NAME": "PARQUET_SOURCE"     }   ] }]

SINK_TYPE#

Defines the Dagger sink type. At present, we support log, influx, kafka, bigquery

  • Example value: log
  • Type: required
  • Default value: influx

FLINK_JOB_ID#

Defines the dagger deployment name

  • Example value: SQL Flink job
  • Type: optional
  • Default value: SQL Flink job

FLINK_ROWTIME_ATTRIBUTE_NAME#

Defines the time attribute field name on the data stream. Find more details on this config here.

  • Example value: rowtime
  • Type: required

FUNCTION_FACTORY_CLASSES#

Defines the factory class of the UDF. Multiple factory classes could be given in a comma-separated format.

  • Example value: FunctionFactory
  • Type: Optional
  • Default value: FunctionFactory

SQL_QUERY#

Defines the SQL query to get the data from the data stream.

  • Example value: SELECT * from data_stream
  • Type: required

SOURCE_KAFKA_CONSUME_LARGE_MESSAGE_ENABLE#

Enable/Disable to consume large messages from Kafka. by default, it's configuration using the default max.partition.fetch.bytes Kafka config. If set to enable, will set the max.partition.fetch.bytes=5242880.

  • Example value: false
  • Type: optional
  • Default value: false

Influx Sink#

An Influx sink Dagger (SINK_TYPE=influx) requires the following variables to be set along with Generic ones.

SINK_INFLUX_URL#

InfluxDB URL, it's usually the hostname followed by port.

  • Example value: http://localhost:8086
  • Type: required

SINK_INFLUX_USERNAME#

Defines the username to connect to InfluxDB.

  • Example value: root
  • Type: required

SINK_INFLUX_PASSWORD#

Defines the password to connect to InfluxDB.

  • Example value: root
  • Type: required

SINK_INFLUX_DB_NAME#

Defines the InfluxDB database name.

  • Example value: DAGGER
  • Type: required

SINK_INFLUX_MEASUREMENT_NAME#

Defines the InfluxDB measurement name. Find more details on using this config here.

  • Example value: concurrent_test
  • Type: required

SINK_INFLUX_RETENTION_POLICY#

Defines the InfluxDB retention policy. Find more details on using this config here.

  • Example value: autogen
  • Type: optional
  • Default value: autogen

SINK_INFLUX_BATCH_SIZE#

Defines the InfluxDB batch size. Find more details on using this config here.

  • Example value: 100
  • Type: optional
  • Default value: 0

SINK_INFLUX_FLUSH_DURATION_MS#

Defines the InfluxDB flush duration in milliseconds. Find more details on using this config here.

  • Example value: 1000
  • Type: optional
  • Default value: 0

Kafka Sink#

A Kafka sink Dagger (SINK_TYPE=kafka) requires the following variables to be set along with Generic ones.

SINK_KAFKA_BROKERS#

Defines the list of Kafka brokers sink.

  • Example value: localhost:9092
  • Type: required

SINK_KAFKA_TOPIC#

Defines the topic of Kafka sink.

  • Example value: test-kafka-output
  • Type: required

SINK_KAFKA_PROTO_KEY#

Defines the proto class key of the data to Kafka sink.

  • Example value: com.tests.OutputKey
  • Type: required

SINK_KAFKA_PROTO_MESSAGE#

Defines the proto class to which the message will get serialized and will be sent to a kafka topic after processing.

  • Example value: com.tests.OutputMessage
  • Type: required

SINK_KAFKA_STREAM#

Defines a name for the Kafka cluster. It's a logical way to name your Kafka clusters.This helps with identifying different kafka cluster the job might be interacting with.

  • Example value: output-stream-name
  • Type: required

SINK_KAFKA_PRODUCE_LARGE_MESSAGE_ENABLE#

Enable/Disable to produce large messages to Kafka. by default, it's configuration using the default max.request.size Kafka config. If set to enable, will set the max.request.size=20971520 and compression.type=snappy.

  • Example value: false
  • Type: optional
  • Default value: false

SINK_KAFKA_LINGER_MS#

Defines the max interval in milliseconds, the producer will wait for the sink/producer buffer to fill.

  • Example value: 1000
  • Type: optional
  • Default value: 0

BigQuery Sink#

A BigQuery sink Dagger (SINK_TYPE=bigquery) requires following env variables to be set along with the Generic Dagger env variables, as well as the Generic and BigQuery env variables in the Raystack Depot repository, since Dagger uses the BigQuery sink connector implementation available in Depot repository.

SINK_BIGQUERY_BATCH_SIZE#

Controls how many records are loaded into the BigQuery Sink in one network call

  • Example value: 500
  • Type: required

SINK_ERROR_TYPES_FOR_FAILURE#

Contains the error types for which the dagger should throw an exception if such an error occurs during runtime. The possible error types are DESERIALIZATION_ERROR, INVALID_MESSAGE_ERROR, UNKNOWN_FIELDS_ERROR, SINK_4XX_ERROR, SINK_5XX_ERROR, SINK_UNKNOWN_ERROR, DEFAULT_ERROR . The error types should be comma-separated.

  • Example value: UNKNOWN_FIELDS_ERROR
  • Type: optional

Schema Registry#

Stencil is dynamic schema registry for protobuf. Find more details about Stencil here.

SCHEMA_REGISTRY_STENCIL_ENABLE#

Enable/Disable using Stencil schema registry.

  • Example value: false
  • Type: optional
  • Default value: false

SCHEMA_REGISTRY_STENCIL_URLS#

Defines the stencil URL. Multiple URLs could be given in a comma-separated format.

  • Example value: http://localhost:8000/testproto.desc
  • Type: required

SCHEMA_REGISTRY_STENCIL_FETCH_TIMEOUT_MS#

Defines the timeout in milliseconds while fetching the descriptor set from the Stencil server.

  • Example value: 607800
  • Type: optional
  • Default value: 60000

SCHEMA_REGISTRY_STENCIL_CACHE_AUTO_REFRESH#

Defines whether to enable/disable the auto schema refresh. Please note that auto schema refresh will only work for additions in Enum types in the Proto. It will not fail for other scenarios but it will just ignore any new field additions at the root or nested level, unless the job is restarted.

  • Example value: true
  • Type: optional
  • Default value: false

SCHEMA_REGISTRY_STENCIL_REFRESH_STRATEGY#

Defines the schema refresh strategy i.e. VERSION_BASED_REFRESH or LONG_POLLING when auto schema refresh is enabled. Please note that if the schema refresh strategy is set to VERSION_BASED_REFRESH then the SCHEMA_REGISTRY_STENCIL_URLS should not be a versioned URL, i.e. it should not have /versions/xx at the end. Also note that VERSION_BASED_REFRESH strategy will only work if you are using a Stencil server as the schema registry.

  • Example value: VERSION_BASED_REFRESH
  • Type: optional
  • Default value: LONG_POLLING

SCHEMA_REGISTRY_STENCIL_CACHE_TTL_MS#

Defines the ttl in milliseconds of the Stencil schema cache after which it will fetch the new descriptors.

  • Example value: 60000
  • Type: optional
  • Default value: 7200000

SCHEMA_REGISTRY_STENCIL_FETCH_BACKOFF_MIN_MS#

Defines the time interval in milliseconds for after which the stencil client will retry to fetch the descriptors after the first failed attempt.

  • Example value: 7000
  • Type: optional
  • Default value: 5000

SCHEMA_REGISTRY_STENCIL_FETCH_RETRIES#

Defines the maximum no. of retries to fetch the descriptors from the Stencil server.

  • Example value: 7
  • Type: optional
  • Default value: 4

Flink#

FLINK_PARALLELISM#

Defines the number of flink parallelism.

  • Example value: 1
  • Type: optional
  • Default value: 1

FLINK_WATERMARK_INTERVAL_MS#

Defines the flink watermark interval in milliseconds. Find more details on this config here.

  • Example value: 10000
  • Type: optional
  • Default value: 10000

FLINK_WATERMARK_DELAY_MS#

Defines the flink watermark delay in milliseconds.

  • Example value: 10000
  • Type: optional
  • Default value: 10000

Note: For a stream configured with PARQUET_SOURCE, the watermark delay should be configured keeping in mind the partitioning that has been used in the root folder containing the files of SOURCE_PARQUET_FILE_PATHS configuration. Currently, only two types of partitioning are supported: day and hour. Hence,

  • if partitioning is day wise, FLINK_WATERMARK_DELAY_MS should be set to 24 x 60 x 60 x 1000 milliseconds, that is, 86400000.
  • if partitioning is hour wise, FLINK_WATERMARK_DELAY_MS should be set to 60 x 60 x 1000 milliseconds, that is, 3600000.

FLINK_WATERMARK_PER_PARTITION_ENABLE#

Enable/Disable flink watermark per partition.

  • Example value: false
  • Type: optional
  • Default value: false

FLINK_CHECKPOINT_INTERVAL_MS#

Find more details about Flink checkpoint here. Defines the flink checkpoint interval in milliseconds.

  • Example value: 30000
  • Type: optional
  • Default value: 30000

FLINK_CHECKPOINT_TIMEOUT_MS#

Defines the flink checkpoint timeout in milliseconds.

  • Example value: 900000
  • Type: optional
  • Default value: 900000

FLINK_CHECKPOINT_MIN_PAUSE_MS#

Defines the minimal pause between checkpointing attempts in milliseconds.

  • Example value: 5000
  • Type: optional
  • Default value: 5000

FLINK_CHECKPOINT_MAX_CONCURRENT#

Defines the maximum number of checkpoint attempts that may be in progress at the same time.

  • Example value: 1
  • Type: optional
  • Default value: 1

FLINK_RETENTION_MIN_IDLE_STATE_HOUR#

Find more details on Flink Idle State Retention here. Defines a minimum time interval for how long idle state in hours.

  • Example value: 8
  • Type: optional
  • Default value: 8

FLINK_RETENTION_MAX_IDLE_STATE_HOUR#

Defines a maximum time interval for how long idle state in hours.

  • Example value: 9
  • Type: optional
  • Default value: 9

Darts#

Darts allows you to join streaming data from the reference data store. Darts provide a reference data store in the form of a list and <key, value> map and enable the refer-table in the form of UDF that can be used through the Flink SQL query.

Details of using Darts can be seen here.

UDF_DART_GCS_PROJECT_ID#

Defines the GCS project id for Dart.

  • Example value: test-project
  • Type: required

UDF_DART_GCS_BUCKET_ID#

Defines the GCS bucket id for Dart.

  • Example value: test-bucket
  • Type: required

Longbow#

Details of using Longbow can be seen here.

PROCESSOR_LONGBOW_ASYNC_TIMEOUT#

Defines the longbow async timeout.

  • Example value: 15000L
  • Type: optional
  • Default value: 15000L

PROCESSOR_LONGBOW_THREAD_CAPACITY#

Defines the longbow thread capacity.

  • Example value: 30
  • Type: optional
  • Default value: 30

PROCESSOR_LONGBOW_GCP_PROJECT_ID#

Defines the GCP project id for longbow.

  • Example value: test-longbow-project
  • Type: required

PROCESSOR_LONGBOW_GCP_INSTANCE_ID#

Defines the GCP instance id for longbow.

  • Example value: test-longbow-instance
  • Type: required

PROCESSOR_LONGBOW_GCP_TABLE_ID#

Defines the GCP Bigtable id for longbow.

  • Example value: test-longbow-table
  • Type: required

PROCESSOR_LONGBOW_DOCUMENT_DURATION#

Defines the longbow document duration.

  • Example value: 90d
  • Type: optional
  • Default value: 90d

will get FLINK_JOB_ID value for this config.

PreProcessor#

PROCESSOR_PREPROCESSOR_ENABLE#

Enable/Disable using pre-processor.

  • Example value: false
  • Type: optional
  • Default value: false

PROCESSOR_PREPROCESSOR_CONFIG#

Details on this configuration can be seen on advance documentation of pre-processor.

PostProcessor#

PROCESSOR_POSTPROCESSOR_ENABLE#

Enable/Disable using post-processor.

  • Example value: false
  • Type: optional
  • Default value: false

PROCESSOR_POSTPROCESSOR_CONFIG#

Details on this configuration can be seen on advance documentation of post-processor.

Telemetry#

METRIC_TELEMETRY_ENABLE#

Enable/Disable the flink telemetry for metric collection

  • Example value: true
  • Type: optional
  • Default value: true

METRIC_TELEMETRY_SHUTDOWN_PERIOD_MS#

Shutdown period of metric telemetry in milliseconds.

  • Example value: 10000
  • Type: optional
  • Default value: 10000

Python Udfs#

PYTHON_UDF_ENABLE#

Enable/Disable using python udf.

  • Example value: 10000
  • Type: optional
  • Default value: 10000

PYTHON_UDF_CONFIG#

All the configuration need to use python udf.

These following variables need to be configured:

PYTHON_FILES#

Defines the path of python udf files. Currently only support for .py and .zip data type. Comma (',') could be used as the separator to specify multiple files.

  • Example value: /path/to/files.zip
  • Type: required
PYTHON_ARCHIVES#

Defines the path of files that used on the python udf. Only support for .zip data type. Comma (',') could be used as the separator to specify multiple archive files. The archive files will be extracted to the working directory of python UDF worker. For each archive file, a target directory is specified. If the target directory name is specified, the archive file will be extracted to a directory with the specified name. Otherwise, the archive file will be extracted to a directory with the same name of the archive file. '#' could be used as the separator of the archive file path and the target directory name.

Example:

  • PYTHON_ARCHIVES=/path/to/data.zip

    You should set the path name to data.zip/data/sample.txt on the udf to be able open the files.

  • PYTHON_ARCHIVES=/path/to/data.zip#data

    You should set the path name to data/sample.txt on the udf to be able open the files.

  • Example how to use this, can be found in this udf

  • Type: optional

  • Default value: (none)

PYTHON_REQUIREMENTS#

Defines the path of python dependency files.

  • Example value: /path/to/requirements.txt
  • Type: optional
  • Default value: (none)
PYTHON_FN_EXECUTION_ARROW_BATCH_SIZE#

The maximum number of elements to include in an arrow batch for python user-defined function execution.

  • Example value: 10000
  • Type: optional
  • Default value: 10000
PYTHON_FN_EXECUTION_BUNDLE_SIZE#

The maximum number of elements to include in a bundle for python user-defined function execution.

  • Example value: 100000
  • Type: optional
  • Default value: 100000
PYTHON_FN_EXECUTION_BUNDLE_TIME#

Sets the waiting timeout(in milliseconds) before processing a bundle for Python user-defined function execution. The timeout defines how long the elements of a bundle will be buffered before being processed.

  • Example value: 1000
  • Type: optional
  • Default value: 1000
Sample Configuration#
PYTHON_UDF_CONFIG = [   {      "PYTHON_FILES": "/path/to/files.py",      "PYTHON_ARCHIVES": "/path/to/data.zip",      "PYTHON_REQUIREMENTS": "/path/to/requirements.txt",      "PYTHON_FN_EXECUTION_ARROW_BATCH_SIZE": "10000",      "PYTHON_FN_EXECUTION_BUNDLE_SIZE": "100000",      "PYTHON_FN_EXECUTION_BUNDLE_TIME": "1000"   }]

Find more details on python udf config here.