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new bdad1bf [HUDI-766]: added section for HoodieMultiTableDeltaStreamer
(#1822)
bdad1bf is described below
commit bdad1bf38190d8f21efde30e549c173b5b9bf115
Author: Pratyaksh Sharma <[email protected]>
AuthorDate: Thu Aug 13 11:59:38 2020 +0530
[HUDI-766]: added section for HoodieMultiTableDeltaStreamer (#1822)
* [HUDI-766]: added section for HoodieMultiTableDeltaStreamer
* [HUDI-766]: small changes
* [HUDI-766]: addressed code review comments
---
docs/_docs/2_2_writing_data.md | 36 ++++++++++++++++++++++++++++++++++++
1 file changed, 36 insertions(+)
diff --git a/docs/_docs/2_2_writing_data.md b/docs/_docs/2_2_writing_data.md
index 6962563..43fc046 100644
--- a/docs/_docs/2_2_writing_data.md
+++ b/docs/_docs/2_2_writing_data.md
@@ -174,6 +174,42 @@ and then ingest it as follows.
In some cases, you may want to migrate your existing table into Hudi
beforehand. Please refer to [migration guide](/docs/migration_guide.html).
+## MultiTableDeltaStreamer
+
+`HoodieMultiTableDeltaStreamer`, a wrapper on top of `HoodieDeltaStreamer`,
enables one to ingest multiple tables at a single go into hudi datasets.
Currently it only supports sequential processing of tables to be ingested and
COPY_ON_WRITE storage type. The command line options for
`HoodieMultiTableDeltaStreamer` are pretty much similar to
`HoodieDeltaStreamer` with the only exception that you are required to provide
table wise configs in separate files in a dedicated config folder. The [...]
+
+```java
+ * --config-folder
+ the path to the folder which contains all the table wise config files
+ --base-path-prefix
+ this is added to enable users to create all the hudi datasets for related
tables under one path in FS. The datasets are then created under the path -
<base_path_prefix>/<database>/<table_to_be_ingested>. However you can override
the paths for every table by setting the property
hoodie.deltastreamer.ingestion.targetBasePath
+```
+
+The following properties are needed to be set properly to ingest data using
`HoodieMultiTableDeltaStreamer`.
+
+```java
+hoodie.deltastreamer.ingestion.tablesToBeIngested
+ comma separated names of tables to be ingested in the format
<database>.<table>, for example db1.table1,db1.table2
+hoodie.deltastreamer.ingestion.targetBasePath
+ if you wish to ingest a particular table in a separate path, you can mention
that path here
+hoodie.deltastreamer.ingestion.<database>.<table>.configFile
+ path to the config file in dedicated config folder which contains table
overridden properties for the particular table to be ingested.
+```
+
+Sample config files for table wise overridden properties can be found under
`hudi-utilities/src/test/resources/delta-streamer-config`. The command to run
`HoodieMultiTableDeltaStreamer` is also similar to how you run
`HoodieDeltaStreamer`.
+
+```java
+[hoodie]$ spark-submit --class
org.apache.hudi.utilities.deltastreamer.HoodieMultiTableDeltaStreamer `ls
packaging/hudi-utilities-bundle/target/hudi-utilities-bundle-*.jar` \
+ --props
file://${PWD}/hudi-utilities/src/test/resources/delta-streamer-config/kafka-source.properties
\
+ --config-folder file://tmp/hudi-ingestion-config \
+ --schemaprovider-class
org.apache.hudi.utilities.schema.SchemaRegistryProvider \
+ --source-class org.apache.hudi.utilities.sources.AvroKafkaSource \
+ --source-ordering-field impresssiontime \
+ --base-path-prefix file:\/\/\/tmp/hudi-deltastreamer-op \
+ --target-table uber.impressions \
+ --op BULK_INSERT
+```
+
## Datasource Writer
The `hudi-spark` module offers the DataSource API to write (and read) a Spark
DataFrame into a Hudi table. There are a number of options available:
