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| Incremental Processing | incremental processing | mydoc_sidebar | incremental_processing.html | false | In this page, we will discuss some available tools for ingesting data incrementally & consuming the changes. |
As discussed in the concepts section, the two basic primitives needed for incrementally processing,
data using Hoodie are upserts (to apply changes to a dataset) and incremental pulls (to obtain a change stream/log from a dataset). This section
discusses a few tools that can be used to achieve these on different contexts.
Incremental Ingestion
Following means can be used to apply a delta or an incremental change to a Hoodie dataset. For e.g, the incremental changes could be from a Kafka topic or files uploaded to HDFS or even changes pulled from another Hoodie dataset.
DeltaStreamer Tool
The HoodieDeltaStreamer utility provides the way to achieve all of these, by using the capabilities of HoodieWriteClient, and support simply row-row ingestion (no transformations)
from different sources such as DFS or Kafka.
The tool is a spark job (part of hoodie-utilities), that provides the following functionality
- Ability to consume new events from Kafka, incremental imports from Sqoop or output of
HiveIncrementalPulleror files under a folder on HDFS - Support json, avro or a custom payload types for the incoming data
- New data is written to a Hoodie dataset, with support for checkpointing & schemas and registered onto Hive
Command line options describe capabilities in more detail (first build hoodie-utilities using mvn clean package).
[hoodie]$ spark-submit --class com.uber.hoodie.utilities.deltastreamer.HoodieDeltaStreamer `ls hoodie-utilities/target/hoodie-utilities-*-SNAPSHOT.jar` --help
Usage: <main class> [options]
Options:
--help, -h
--key-generator-class
Subclass of com.uber.hoodie.KeyGenerator to generate a HoodieKey from
the given avro record. Built in: SimpleKeyGenerator (uses provided field
names as recordkey & partitionpath. Nested fields specified via dot
notation, e.g: a.b.c)
Default: com.uber.hoodie.SimpleKeyGenerator
--op
Takes one of these values : UPSERT (default), INSERT (use when input is
purely new data/inserts to gain speed)
Default: UPSERT
Possible Values: [UPSERT, INSERT, BULK_INSERT]
--payload-class
subclass of HoodieRecordPayload, that works off a GenericRecord.
Implement your own, if you want to do something other than overwriting
existing value
Default: com.uber.hoodie.OverwriteWithLatestAvroPayload
--props
path to properties file on localfs or dfs, with configurations for
hoodie client, schema provider, key generator and data source. For
hoodie client props, sane defaults are used, but recommend use to
provide basic things like metrics endpoints, hive configs etc. For
sources, referto individual classes, for supported properties.
Default: file:///Users/vinoth/bin/hoodie/src/test/resources/delta-streamer-config/dfs-source.properties
--schemaprovider-class
subclass of com.uber.hoodie.utilities.schema.SchemaProvider to attach
schemas to input & target table data, built in options:
FilebasedSchemaProvider
Default: com.uber.hoodie.utilities.schema.FilebasedSchemaProvider
--source-class
Subclass of com.uber.hoodie.utilities.sources to read data. Built-in
options: com.uber.hoodie.utilities.sources.{JsonDFSSource (default),
AvroDFSSource, JsonKafkaSource, AvroKafkaSource, HiveIncrPullSource}
Default: com.uber.hoodie.utilities.sources.JsonDFSSource
--source-limit
Maximum amount of data to read from source. Default: No limit For e.g:
DFSSource => max bytes to read, KafkaSource => max events to read
Default: 9223372036854775807
--source-ordering-field
Field within source record to decide how to break ties between records
with same key in input data. Default: 'ts' holding unix timestamp of
record
Default: ts
--spark-master
spark master to use.
Default: local[2]
* --target-base-path
base path for the target hoodie dataset. (Will be created if did not
exist first time around. If exists, expected to be a hoodie dataset)
* --target-table
name of the target table in Hive
The tool takes a hierarchically composed property file and has pluggable interfaces for extracting data, key generation and providing schema. Sample configs for ingesting from kafka and dfs are
provided under hoodie-utilities/src/test/resources/delta-streamer-config.
For e.g: once you have Confluent Kafka, Schema registry up & running, produce some test data using (impressions.avro provided by schema-registry repo)
[confluent-5.0.0]$ bin/ksql-datagen schema=../impressions.avro format=avro topic=impressions key=impressionid
and then ingest it as follows.
[hoodie]$ spark-submit --class com.uber.hoodie.utilities.deltastreamer.HoodieDeltaStreamer `ls hoodie-utilities/target/hoodie-utilities-*-SNAPSHOT.jar` \
--props file://${PWD}/hoodie-utilities/src/test/resources/delta-streamer-config/kafka-source.properties \
--schemaprovider-class com.uber.hoodie.utilities.schema.SchemaRegistryProvider \
--source-class com.uber.hoodie.utilities.sources.AvroKafkaSource \
--source-ordering-field impresssiontime \
--target-base-path file:///tmp/hoodie-deltastreamer-op --target-table uber.impressions \
--op BULK_INSERT
In some cases, you may want to convert your existing dataset into Hoodie, before you can begin ingesting new data. This can be accomplished using the hdfsparquetimport command on the hoodie-cli.
Currently, there is support for converting parquet datasets.
Via Custom Spark Job
The hoodie-spark module offers the DataSource API to write any data frame into a Hoodie dataset. Following is how we can upsert a dataframe, while specifying the field names that need to be used
for recordKey => _row_key, partitionPath => partition and precombineKey => timestamp
inputDF.write()
.format("com.uber.hoodie")
.options(clientOpts) // any of the hoodie client opts can be passed in as well
.option(DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY(), "_row_key")
.option(DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY(), "partition")
.option(DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY(), "timestamp")
.option(HoodieWriteConfig.TABLE_NAME, tableName)
.mode(SaveMode.Append)
.save(basePath);
Please refer to configurations section, to view all datasource options.
Syncing to Hive
Once new data is written to a Hoodie dataset, via tools like above, we need the ability to sync with Hive and reflect the table schema such that queries can pick up new columns and partitions. To do this, Hoodie provides a HiveSyncTool, which can be
invoked as below, once you have built the hoodie-hive module.
[hoodie-hive]$ java -cp target/hoodie-hive-0.3.6-SNAPSHOT-jar-with-dependencies.jar:target/jars/* com.uber.hoodie.hive.HiveSyncTool --help
Usage: <main class> [options]
Options:
* --base-path
Basepath of hoodie dataset to sync
* --database
name of the target database in Hive
--help, -h
Default: false
* --jdbc-url
Hive jdbc connect url
* --pass
Hive password
* --table
name of the target table in Hive
* --user
Hive username
{% include callout.html content="Note that for now, due to jar mismatches between Spark & Hive, its recommended to run this as a separate Java task in your workflow manager/cron. This is getting fix here" type="info" %}
Incrementally Pulling
Hoodie datasets can be pulled incrementally, which means you can get ALL and ONLY the updated & new rows since a specified commit timestamp. This, together with upserts, are particularly useful for building data pipelines where 1 or more source hoodie tables are incrementally pulled (streams/facts), joined with other tables (datasets/dimensions), to produce deltas to a target hoodie dataset. Then, using the delta streamer tool these deltas can be upserted into the target hoodie dataset to complete the pipeline.
Via Spark Job
The hoodie-spark module offers the DataSource API, offers a more elegant way to pull data from Hoodie dataset (plus more) and process it via Spark.
This class can be used within existing Spark jobs and offers the following functionality.
A sample incremental pull, that will obtain all records written since beginInstantTime, looks like below.
Dataset<Row> hoodieIncViewDF = spark.read()
.format("com.uber.hoodie")
.option(DataSourceReadOptions.VIEW_TYPE_OPT_KEY(),
DataSourceReadOptions.VIEW_TYPE_INCREMENTAL_OPT_VAL())
.option(DataSourceReadOptions.BEGIN_INSTANTTIME_OPT_KEY(),
<beginInstantTime>)
.load(tablePath); // For incremental view, pass in the root/base path of dataset
Please refer to configurations section, to view all datasource options.
Additionally, HoodieReadClient offers the following functionality using Hoodie's implicit indexing.
| API | Description | | read(keys) | Read out the data corresponding to the keys as a DataFrame, using Hoodie's own index for faster lookup | | filterExists() | Filter out already existing records from the provided RDD[HoodieRecord]. Useful for de-duplication | | checkExists(keys) | Check if the provided keys exist in a Hoodie dataset |
HiveIncrementalPuller Tool
HiveIncrementalPuller allows the above to be done via HiveQL, combining the benefits of Hive (reliably process complex SQL queries) and incremental primitives
(speed up query by pulling tables incrementally instead of scanning fully). The tool uses Hive JDBC to run the Hive query saving its results in a temp table.
that can later be upserted. Upsert utility (HoodieDeltaStreamer) has all the state it needs from the directory structure to know what should be the commit time on the target table.
e.g: /app/incremental-hql/intermediate/{source_table_name}_temp/{last_commit_included}.The Delta Hive table registered will be of the form {tmpdb}.{source_table}_{last_commit_included}.
The following are the configuration options for HiveIncrementalPuller
| Config | Description | Default | |hiveUrl| Hive Server 2 URL to connect to | | |hiveUser| Hive Server 2 Username | | |hivePass| Hive Server 2 Password | | |queue| YARN Queue name | | |tmp| Directory where the temporary delta data is stored in HDFS. The directory structure will follow conventions. Please see the below section. | | |extractSQLFile| The SQL to execute on the source table to extract the data. The data extracted will be all the rows that changed since a particular point in time. | | |sourceTable| Source Table Name. Needed to set hive environment properties. | | |targetTable| Target Table Name. Needed for the intermediate storage directory structure. | | |sourceDataPath| Source HDFS Base Path. This is where the hoodie metadata will be read. | | |targetDataPath| Target HDFS Base path. This is needed to compute the fromCommitTime. This is not needed if fromCommitTime is specified explicitly. | | |tmpdb| The database to which the intermediate temp delta table will be created | hoodie_temp | |fromCommitTime| This is the most important parameter. This is the point in time from which the changed records are pulled from. | | |maxCommits| Number of commits to include in the pull. Setting this to -1 will include all the commits from fromCommitTime. Setting this to a value > 0, will include records that ONLY changed in the specified number of commits after fromCommitTime. This may be needed if you need to catch up say 2 commits at a time. | 3 | |help| Utility Help | |
Setting the fromCommitTime=0 and maxCommits=-1 will pull in the entire source dataset and can be used to initiate backfills. If the target dataset is a hoodie dataset, then the utility can determine if the target dataset has no commits or is behind more than 24 hour (this is configurable), it will automatically use the backfill configuration, since applying the last 24 hours incrementally could take more time than doing a backfill. The current limitation of the tool is the lack of support for self-joining the same table in mixed mode (normal and incremental modes).