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https://issues.apache.org/jira/browse/HUDI-1548?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17383129#comment-17383129
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ASF GitHub Bot commented on HUDI-1548:
--------------------------------------
danny0405 commented on a change in pull request #3257:
URL: https://github.com/apache/hudi/pull/3257#discussion_r672081905
##########
File path: docs/_docs/2_2_writing_data.md
##########
@@ -424,3 +424,192 @@ Here are some ways to efficiently manage the storage of
your Hudi tables.
- Intelligently tuning the [bulk insert
parallelism](/docs/configurations.html#withBulkInsertParallelism), can again in
nicely sized initial file groups. It is in fact critical to get this right,
since the file groups
once created cannot be deleted, but simply expanded as explained before.
- For workloads with heavy updates, the [merge-on-read
table](/docs/concepts.html#merge-on-read-table) provides a nice mechanism for
ingesting quickly into smaller files and then later merging them into larger
base files via compaction.
+
+
+## Schema Evolution
+
+Schema evolution is a very important aspect of data management.
+Hudi supports common schema evolution scenarios, such as adding a nullable
field or promoting a datatype of a field, out-of-the-box.
+Furthermore, the evolved schema is queryable across engines, such as Presto,
Hive and Spark SQL.
+The following table presents a summary of the types of schema changes
compatible with different Hudi table types.
+
+| Schema Change | COW | MOR | Remarks |
+| ----------- | ------- | ------- | ------- |
+| Add a new nullable column at root level at the end | Yes | Yes | `Yes` means
that a write with evolved schema succeeds and a read following the write
succeeds to read entire dataset. |
+| Add a new nullable column to inner struct (at the end) | Yes | Yes |
+| Add a new complex type field with default (map and array) | Yes | Yes | |
+| Add a new nullable column and change the ordering of fields | Yes | Yes | |
+| Add a custom nullable Hudi meta column, e.g. `_hoodie_meta_col` | Yes | Yes
| |
+| Promote datatype from `int` to `long` for a field at root level | Yes | Yes
| For other types, Hudi supports promotion as specified in [Avro schema
resolution](http://avro.apache.org/docs/current/spec.html#Schema+Resolution). |
+| Promote datatype from `int` to `long` for a nested field | Yes | Yes |
+| Promote datatype from `int` to `long` for a complex type (value of map or
array) | Yes | Yes | |
+| Add a new non-nullable column at root level at the end | No | No | In case
of MOR table with Spark data source, write succeeds but read fails. |
+| Add a new non-nullable column to inner struct (at the end) | No | No | |
+| Change datatype from `long` to `int` for a nested field | No | No | |
+| Change datatype from `long` to `int` for a complex type (value of map or
array) | No | No | |
+
+Let us walk through an example to demonstrate the schema evolution support in
Hudi.
+In the below example, we are going to add a new string field and change the
datatype of a field from int to long.
+
+```java
+Welcome to
+ ____ __
+ / __/__ ___ _____/ /__
+ _\ \/ _ \/ _ `/ __/ '_/
+ /___/ .__/\_,_/_/ /_/\_\ version 3.1.2
+ /_/
+
+ Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_292)
+ Type in expressions to have them evaluated.
+ Type :help for more information.
+
+scala> import org.apache.hudi.QuickstartUtils._
+import org.apache.hudi.QuickstartUtils._
+
+scala> import scala.collection.JavaConversions._
+import scala.collection.JavaConversions._
+
+scala> import org.apache.spark.sql.SaveMode._
+import org.apache.spark.sql.SaveMode._
+
+scala> import org.apache.hudi.DataSourceReadOptions._
+import org.apache.hudi.DataSourceReadOptions._
+
+scala> import org.apache.hudi.DataSourceWriteOptions._
+import org.apache.hudi.DataSourceWriteOptions._
+
+scala> import org.apache.hudi.config.HoodieWriteConfig._
+import org.apache.hudi.config.HoodieWriteConfig._
+
+scala> import org.apache.spark.sql.types._
+import org.apache.spark.sql.types._
+
+scala> import org.apache.spark.sql.Row
+import org.apache.spark.sql.Row
+
+scala> val tableName = "hudi_trips_cow"
+ tableName: String = hudi_trips_cow
+scala> val basePath = "file:///tmp/hudi_trips_cow"
+ basePath: String = file:///tmp/hudi_trips_cow
+scala> val schema = StructType( Array(
+ | StructField("rowId", StringType,true),
+ | StructField("partitionId", StringType,true),
+ | StructField("preComb", LongType,true),
+ | StructField("name", StringType,true),
+ | StructField("versionId", StringType,true),
+ | StructField("intToLong", IntegerType,true)
+ | ))
+ schema: org.apache.spark.sql.types.StructType =
StructType(StructField(rowId,StringType,true),
StructField(partitionId,StringType,true), StructField(preComb,LongType,true),
StructField(name,StringType,true), StructField(versionId,StringType,true),
StructField(intToLong,IntegerType,true))
+
+scala> val data1 = Seq(Row("row_1", "part_0", 0L, "bob", "v_0", 0),
+ | Row("row_2", "part_0", 0L, "john", "v_0", 0),
+ | Row("row_3", "part_0", 0L, "tom", "v_0", 0))
+ data1: Seq[org.apache.spark.sql.Row] = List([row_1,part_0,0,bob,v_0,0],
[row_2,part_0,0,john,v_0,0], [row_3,part_0,0,tom,v_0,0])
+
+scala> var dfFromData1 = spark.createDataFrame(data1, schema)
+scala> dfFromData1.write.format("hudi").
+ | options(getQuickstartWriteConfigs).
+ | option(PRECOMBINE_FIELD_OPT_KEY.key, "preComb").
+ | option(RECORDKEY_FIELD_OPT_KEY.key, "rowId").
+ | option(PARTITIONPATH_FIELD_OPT_KEY.key, "partitionId").
+ | option("hoodie.index.type","SIMPLE").
+ | option(TABLE_NAME.key, tableName).
+ | mode(Overwrite).
+ | save(basePath)
+
+scala> var tripsSnapshotDF1 = spark.read.format("hudi").load(basePath + "/*/*")
+ tripsSnapshotDF1: org.apache.spark.sql.DataFrame = [_hoodie_commit_time:
string, _hoodie_commit_seqno: string ... 9 more fields]
+
+scala> tripsSnapshotDF1.createOrReplaceTempView("hudi_trips_snapshot")
+
+scala> spark.sql("desc hudi_trips_snapshot").show()
+ +--------------------+---------+-------+
+ | col_name|data_type|comment|
+ +--------------------+---------+-------+
+ | _hoodie_commit_time| string| null|
+ |_hoodie_commit_seqno| string| null|
+ | _hoodie_record_key| string| null|
+ |_hoodie_partition...| string| null|
+ | _hoodie_file_name| string| null|
+ | rowId| string| null|
+ | partitionId| string| null|
+ | preComb| bigint| null|
+ | name| string| null|
+ | versionId| string| null|
+ | intToLong| int| null|
+ +--------------------+---------+-------+
+
+scala> spark.sql("select rowId, partitionId, preComb, name, versionId,
intToLong from hudi_trips_snapshot").show()
+ +-----+-----------+-------+----+---------+---------+
+ |rowId|partitionId|preComb|name|versionId|intToLong|
+ +-----+-----------+-------+----+---------+---------+
+ |row_3| part_0| 0| tom| v_0| 0|
+ |row_2| part_0| 0|john| v_0| 0|
+ |row_1| part_0| 0| bob| v_0| 0|
+ +-----+-----------+-------+----+---------+---------+
+
+// In the new schema, we are going to add a String field and
+// change the datatype `intToLong` field from int to long.
+scala> val newSchema = StructType( Array(
+ | StructField("rowId", StringType,true),
+ | StructField("partitionId", StringType,true),
+ | StructField("preComb", LongType,true),
+ | StructField("name", StringType,true),
+ | StructField("versionId", StringType,true),
+ | StructField("intToLong", LongType,true),
+ | StructField("newField", StringType,true)
+ | ))
+ newSchema: org.apache.spark.sql.types.StructType =
StructType(StructField(rowId,StringType,true),
StructField(partitionId,StringType,true), StructField(preComb,LongType,true),
StructField(name,StringType,true), StructField(versionId,StringType,true),
StructField(intToLong,LongType,true), StructField(newField,StringType,true))
+
+scala> val data2 = Seq(Row("row_2", "part_0", 5L, "john", "v_3", 3L,
"newField_1"),
+ | Row("row_5", "part_0", 5L, "maroon", "v_2", 2L,
"newField_1"),
+ | Row("row_9", "part_0", 5L, "michael", "v_2", 2L,
"newField_1"))
+ data2: Seq[org.apache.spark.sql.Row] =
List([row_2,part_0,5,john,v_3,3,newField_1],
[row_5,part_0,5,maroon,v_2,2,newField_1],
[row_9,part_0,5,michael,v_2,2,newField_1])
+
+scala> var dfFromData2 = spark.createDataFrame(data2, newSchema)
+scala> dfFromData2.write.format("hudi").
+ | options(getQuickstartWriteConfigs).
+ | option(PRECOMBINE_FIELD_OPT_KEY.key, "preComb").
+ | option(RECORDKEY_FIELD_OPT_KEY.key, "rowId").
+ | option(PARTITIONPATH_FIELD_OPT_KEY.key, "partitionId").
+ | option("hoodie.index.type","SIMPLE").
+ | option(TABLE_NAME.key, tableName).
+ | mode(Append).
+ | save(basePath)
+
+scala> var tripsSnapshotDF2 = spark.read.format("hudi").load(basePath + "/*/*")
+ tripsSnapshotDF2: org.apache.spark.sql.DataFrame = [_hoodie_commit_time:
string, _hoodie_commit_seqno: string ... 10 more fields]
+
Review comment:
One question i'm curious about: does the table schema changed when new
schema data set writes successfully. When and how ?
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> Fix documentation around schema evolution
> ------------------------------------------
>
> Key: HUDI-1548
> URL: https://issues.apache.org/jira/browse/HUDI-1548
> Project: Apache Hudi
> Issue Type: Improvement
> Components: Docs
> Reporter: sivabalan narayanan
> Assignee: Nishith Agarwal
> Priority: Blocker
> Labels: ', pull-request-available, sev:high, user-support-issues
> Fix For: 0.9.0
>
>
> Clearly call out what kind of schema evolution is supported by hudi in
> documentation .
> Context: https://github.com/apache/hudi/issues/2331
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