[
https://issues.apache.org/jira/browse/HUDI-2063?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17397295#comment-17397295
]
ASF GitHub Bot commented on HUDI-2063:
--------------------------------------
pengzhiwei2018 commented on a change in pull request #3140:
URL: https://github.com/apache/hudi/pull/3140#discussion_r686733674
##########
File path: website/docs/querying_data.md
##########
@@ -119,6 +119,11 @@ By default, Spark SQL will try to use its own parquet
reader instead of Hive Ser
both parquet and avro data, this default setting needs to be turned off using
set `spark.sql.hive.convertMetastoreParquet=false`.
This will force Spark to fallback to using the Hive Serde to read the data
(planning/executions is still Spark).
+**NOTICE**
+
+Since 0.9.0 hudi will sync the table to hive as a spark datasource table. So
we do not need the `spark.sql.hive.convertMetastoreParquet=false`
Review comment:
Well, for an existing old table, the hive sync will not update the
properties of spark datasource table to the table. So they still need this
config when querying.
##########
File path: website/docs/quick-start-guide.md
##########
@@ -263,6 +451,88 @@ df.write.format("hudi").
save(basePath)
```
+</TabItem>
+<TabItem value="sparksql">
+
+Spark sql support two kinds of DML to udpate hudi table: Merge-Into and Update.
+
+###MergeInto
+
+Hudi support merge-into for both spark 2 & spark 3.
+
+**Syntax**
+
+```sql
+MERGE INTO tableIdentifier AS target_alias
+USING (sub_query | tableIdentifier) AS source_alias
+ON <merge_condition>
+[ WHEN MATCHED [ AND <condition> ] THEN <matched_action> ]
+[ WHEN MATCHED [ AND <condition> ] THEN <matched_action> ]
+[ WHEN NOT MATCHED [ AND <condition> ] THEN <not_matched_action> ]
+
+<merge_condition> =A equal bool condition
+<matched_action> =
+ DELETE |
+ UPDATE SET * |
+ UPDATE SET column1 = expression1 [, column2 = expression2 ...]
+<not_matched_action> =
+ INSERT * |
+ INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 ...])
+```
+**Case**
+```sql
+merge into h0 as target
+using (
+ select id, name, price, flag from s
+) source
+on target.id = source.id
+when matched then update set *
+when not matched then insert *
+;
+
+merge into h0
+using (
+ select id, name, price, flag from s
+) source
+on h0.id = source.id
+when matched and flag != 'delete' then update set id = source.id, name =
source.name, price = source.price * 2
+when matched and flag = 'delete' then delete
+when not matched then insert (id,name,price) values(id, name, price)
+;
+```
+**Notice**
+
+1、The merge-on condition must be the primary keys.
+2、Merge-On-Read table do not support partial. e.g.
+```sql
+ merge into h0 using s0
+ on h0.id = s0.id
+ when matched then update set price = s0.price * 2
+```
+This works well for Cow-On-Write table which support update only the **price**
field. But it do not work
+for Merge-ON-READ table.
Review comment:
Make sense.
##########
File path: website/docs/quick-start-guide.md
##########
@@ -22,26 +22,49 @@ defaultValue="scala"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
+{ label: 'SparkSQL', value: 'sparksql', },
]}>
<TabItem value="scala">
```scala
// spark-shell for spark 3
spark-shell \
- --packages
org.apache.hudi:hudi-spark3-bundle_2.12:0.8.0,org.apache.spark:spark-avro_2.12:3.0.1
\
+ --packages
org.apache.hudi:hudi-spark3-bundle_2.12:0.9.0,org.apache.spark:spark-avro_2.12:3.0.1
\
Review comment:
OK
##########
File path: website/docs/querying_data.md
##########
@@ -141,14 +146,13 @@ and executors. Alternatively, hudi-spark-bundle can also
fetched via the `--pack
### Snapshot query {#spark-snap-query}
This method can be used to retrieve the data table at the present point in
time.
-Note: The file path must be suffixed with a number of wildcard asterisk (`/*`)
one greater than the number of partition levels. Eg: with table file path
"tablePath" partitioned by columns "a", "b", and "c", the load path must be
`tablePath + "/*/*/*/*"`
```scala
val hudiIncQueryDF = spark
.read()
- .format("org.apache.hudi")
+ .format("hudi")
.option(DataSourceReadOptions.QUERY_TYPE_OPT_KEY(),
DataSourceReadOptions.QUERY_TYPE_SNAPSHOT_OPT_VAL())
- .load(tablePath + "/*") //The number of wildcard asterisks here must be
one greater than the number of partition
+ .load(tablePath)
Review comment:
Yes, since 0.9.0 version, we support HoodieFileIndex, which does not
need the "*"
##########
File path: website/docs/quick-start-guide.md
##########
@@ -22,26 +22,49 @@ defaultValue="scala"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
+{ label: 'SparkSQL', value: 'sparksql', },
]}>
<TabItem value="scala">
```scala
// spark-shell for spark 3
spark-shell \
- --packages
org.apache.hudi:hudi-spark3-bundle_2.12:0.8.0,org.apache.spark:spark-avro_2.12:3.0.1
\
+ --packages
org.apache.hudi:hudi-spark3-bundle_2.12:0.9.0,org.apache.spark:spark-avro_2.12:3.0.1
\
--conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
// spark-shell for spark 2 with scala 2.12
spark-shell \
- --packages
org.apache.hudi:hudi-spark-bundle_2.12:0.8.0,org.apache.spark:spark-avro_2.12:2.4.4
\
+ --packages
org.apache.hudi:hudi-spark-bundle_2.12:0.9.0,org.apache.spark:spark-avro_2.12:2.4.4
\
--conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
// spark-shell for spark 2 with scala 2.11
spark-shell \
- --packages
org.apache.hudi:hudi-spark-bundle_2.11:0.8.0,org.apache.spark:spark-avro_2.11:2.4.4
\
+ --packages
org.apache.hudi:hudi-spark-bundle_2.11:0.9.0,org.apache.spark:spark-avro_2.11:2.4.4
\
--conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
```
+</TabItem>
+<TabItem value="sparksql">
+
+Hudi support using spark sql to write and read data with the
**HoodieSparkSessionExtension** sql extension.
+```shell
+# spark sql for spark 3
+spark-sql --packages
org.apache.hudi:hudi-spark3-bundle_2.12:0.9.0,org.apache.spark:spark-avro_2.12:3.0.1
\
Review comment:
Ok
##########
File path: website/docs/quick-start-guide.md
##########
@@ -146,6 +249,55 @@ df.write.format("hudi").
save(basePath)
```
+</TabItem>
+<TabItem value="sparksql">
+
+```sql
+insert into h0 select 1, 'a1', 20;
+
+-- insert static partition
+insert into h_p0 partition(dt = '2021-01-02') select 1, 'a1';
+
+-- insert dynamic partition
+insert into h_p0 select 1, 'a1', dt;
+
+-- insert dynamic partition
+insert into h_p1 select 1 as id, 'a1', '2021-01-03' as dt, '19' as hh;
+
+-- insert overwrite table
+insert overwrite table h0 select 1, 'a1', 20;
+
+-- insert overwrite table with static partition
+insert overwrite h_p0 partition(dt = '2021-01-02') select 1, 'a1';
+
+- insert overwrite table with dynamic partition
+ insert overwrite table h_p1 select 2 as id, 'a2', '2021-01-03' as dt, '19'
as hh;
+```
+
+**NOTICE**
+
+1. Insert mode
+
+Hudi support three insert modes when inserting data to a table with primary
key(we call it pk-table as followed):
+- upsert
+
+ This it the default insert mode. For upsert mode, insert statement do the
upsert operation for the pk-table which will update the duplicate record
+- strict
+
+For strict mode, insert statement will keep the primary key uniqueness
constraint for COW table which do not allow duplicate record.
+If inserting a record which the primary key is already exists to the table, a
HoodieDuplicateKeyException will throw out
+for COW table. For MOR table, it has the same behavior with "upsert" mode.
+
+- non-strict
+
+For non-strict mode, hudi just do the insert operation for the pk-table.
+
+We can set the inset mode by the config: **hoodie.sql.insert.mode**
Review comment:
done!
##########
File path: website/docs/quick-start-guide.md
##########
@@ -533,12 +816,16 @@ df.write.format("hudi").
// Should have different keys now, from query before.
spark.
read.format("hudi").
- load(basePath + "/*/*/*/*").
+ load(basePath).
select("uuid","partitionpath").
show(10, false)
```
+**NOTICE**
+
+The insert overwrite non-partitioned table sql statement will convert to the
***insert_overwrite_table*** operation.
Review comment:
make sense.
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> [SQL] Add Doc For Spark Sql Integrates With Hudi
> ------------------------------------------------
>
> Key: HUDI-2063
> URL: https://issues.apache.org/jira/browse/HUDI-2063
> Project: Apache Hudi
> Issue Type: Sub-task
> Components: Docs
> Reporter: pengzhiwei
> Assignee: pengzhiwei
> Priority: Blocker
> Labels: pull-request-available, release-blocker
> Fix For: 0.9.0
>
>
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