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Posted to commits@hudi.apache.org by GitBox <gi...@apache.org> on 2020/05/02 15:34:27 UTC
[GitHub] [incubator-hudi] lamber-ken commented on a change in pull request #1526: [HUDI-783] Add pyspark example in quickstart
lamber-ken commented on a change in pull request #1526:
URL: https://github.com/apache/incubator-hudi/pull/1526#discussion_r418972065
##########
File path: docs/_docs/1_1_quick_start_guide.md
##########
@@ -204,6 +213,224 @@ spark.sql("select uuid, partitionPath from hudi_trips_snapshot").count()
```
Note: Only `Append` mode is supported for delete operation.
+# Pyspark example
+## Setup
+
+Hudi works with Spark-2.x versions. You can follow instructions [here](https://spark.apache.org/downloads.html) for setting up spark.
+From the extracted directory run spark-shell with Hudi as:
+
+```python
+# pyspark
+export PYSPARK_PYTHON=$(which python3)
+spark-2.4.4-bin-hadoop2.7/bin/pyspark \
+ --packages org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4 \
+ --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
+```
+
+<div class="notice--info">
+ <h4>Please note the following: </h4>
+<ul>
+ <li>spark-avro module needs to be specified in --packages as it is not included with spark-shell by default</li>
+ <li>spark-avro and spark versions must match (we have used 2.4.4 for both above)</li>
+ <li>we have used hudi-spark-bundle built for scala 2.11 since the spark-avro module used also depends on 2.11.
+ If spark-avro_2.12 is used, correspondingly hudi-spark-bundle_2.12 needs to be used. </li>
+</ul>
+</div>
+
+Setup table name, base path and a data generator to generate records for this guide.
+
+```python
+# pyspark
+tableName = "hudi_trips_cow"
+basePath = "file:///tmp/hudi_trips_cow"
+dataGen = sc._jvm.org.apache.hudi.QuickstartUtils.DataGenerator()
+```
+
+The [DataGenerator](https://github.com/apache/incubator-hudi/blob/master/hudi-spark/src/main/java/org/apache/hudi/QuickstartUtils.java#L50)
+can generate sample inserts and updates based on the the sample trip schema [here](https://github.com/apache/incubator-hudi/blob/master/hudi-spark/src/main/java/org/apache/hudi/QuickstartUtils.java#L57)
+{: .notice--info}
+
+
+## Insert data
+
+Generate some new trips, load them into a DataFrame and write the DataFrame into the Hudi table as below.
+
+```python
+# pyspark
+inserts = sc._jvm.org.apache.hudi.QuickstartUtils.convertToStringList(dataGen.generateInserts(10))
+df = spark.read.json(spark.sparkContext.parallelize(inserts, 2))
+
+hudi_options = {
+ 'hoodie.table.name': tableName,
+ 'hoodie.datasource.write.recordkey.field': 'uuid',
+ 'hoodie.datasource.write.partitionpath.field': 'partitionpath',
+ 'hoodie.datasource.write.table.name': tableName,
+ 'hoodie.datasource.write.operation': 'insert',
+ 'hoodie.datasource.write.precombine.field': 'ts',
+ 'hoodie.upsert.shuffle.parallelism': 2,
+ 'hoodie.insert.shuffle.parallelism': 2
+}
+
+df.write.format("hudi"). \
+ options(**hudi_options). \
+ mode("overwrite"). \
+ save(basePath)
+```
+
+`mode(Overwrite)` overwrites and recreates the table if it already exists.
+You can check the data generated under `/tmp/hudi_trips_cow/<region>/<country>/<city>/`. We provided a record key
+(`uuid` in [schema](https://github.com/apache/incubator-hudi/blob/master/hudi-spark/src/main/java/org/apache/hudi/QuickstartUtils.java#L58)), partition field (`region/county/city`) and combine logic (`ts` in
+[schema](https://github.com/apache/incubator-hudi/blob/master/hudi-spark/src/main/java/org/apache/hudi/QuickstartUtils.java#L58)) to ensure trip records are unique within each partition. For more info, refer to
+[Modeling data stored in Hudi](https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=113709185#FAQ-HowdoImodelthedatastoredinHudi)
+and for info on ways to ingest data into Hudi, refer to [Writing Hudi Tables](/docs/writing_data.html).
+Here we are using the default write operation : `upsert`. If you have a workload without updates, you can also issue
+`insert` or `bulk_insert` operations which could be faster. To know more, refer to [Write operations](/docs/writing_data#write-operations)
+{: .notice--info}
+
+## Query data
+
+Load the data files into a DataFrame.
+
+```python
+# pyspark
+tripsSnapshotDF = spark. \
+ read. \
+ format("hudi"). \
+ load(basePath + "/*/*/*/*")
+
+tripsSnapshotDF.createOrReplaceTempView("hudi_trips_snapshot")
+
+spark.sql("select fare, begin_lon, begin_lat, ts from hudi_trips_snapshot where fare > 20.0").show()
+spark.sql("select _hoodie_commit_time, _hoodie_record_key, _hoodie_partition_path, rider, driver, fare from hudi_trips_snapshot").show()
+```
+
+This query provides snapshot querying of the ingested data. Since our partition path (`region/country/city`) is 3 levels nested
+from base path we ve used `load(basePath + "/*/*/*/*")`.
+Refer to [Table types and queries](/docs/concepts#table-types--queries) for more info on all table types and query types supported.
+{: .notice--info}
+
+## Update data
+
+This is similar to inserting new data. Generate updates to existing trips using the data generator, load into a DataFrame
+and write DataFrame into the hudi table.
+
+```python
+# pyspark
+updates = sc._jvm.org.apache.hudi.QuickstartUtils.convertToStringList(dataGen.generateUpdates(10))
+df = spark.read.json(spark.sparkContext.parallelize(updates, 2))
+df.write.format("hudi"). \
+ options(**hudi_options). \
+ mode("append"). \
+ save(basePath)
+```
+
+Notice that the save mode is now `Append`. In general, always use append mode unless you are trying to create the table for the first time.
+[Querying](#query-data) the data again will now show updated trips. Each write operation generates a new [commit](http://hudi.incubator.apache.org/docs/concepts.html)
+denoted by the timestamp. Look for changes in `_hoodie_commit_time`, `rider`, `driver` fields for the same `_hoodie_record_key`s in previous commit.
+{: .notice--info}
+
+## Incremental query
+
+Hudi also provides capability to obtain a stream of records that changed since given commit timestamp.
+This can be achieved using Hudi's incremental querying and providing a begin time from which changes need to be streamed.
+We do not need to specify endTime, if we want all changes after the given commit (as is the common case).
+
+```python
+# pyspark
+# reload data
+spark. \
+ read. \
+ format("hudi"). \
+ load(basePath + "/*/*/*/*"). \
+ createOrReplaceTempView("hudi_trips_snapshot")
+
+commits = list(map(lambda row: row[0], spark.sql("select distinct(_hoodie_commit_time) as commitTime from hudi_trips_snapshot order by commitTime").limit(50).collect()))
+beginTime = commits[len(commits) - 2] # commit time we are interested in
+
+# incrementally query data
+incremental_read_options = {
+ 'hoodie.datasource.query.type': 'incremental',
+ 'hoodie.datasource.read.begin.instanttime': beginTime,
+}
+
+tripsIncrementalDF = spark.read.format("hudi"). \
+ options(**incremental_read_options). \
+ load(basePath)
+tripsIncrementalDF.createOrReplaceTempView("hudi_trips_incremental")
+
+spark.sql("select `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts from hudi_trips_incremental where fare > 20.0").show()
+```
+
+This will give all changes that happened after the beginTime commit with the filter of fare > 20.0. The unique thing about this
+feature is that it now lets you author streaming pipelines on batch data.
+{: .notice--info}
+
+## Point in time query
+
+Lets look at how to query data as of a specific time. The specific time can be represented by pointing endTime to a
+specific commit time and beginTime to "000" (denoting earliest possible commit time).
+
+```python
+# pyspark
+beginTime = "000" # Represents all commits > this time.
+endTime = commits[len(commits) - 2]
+
+# query point in time data
+point_in_time_read_options = {**incremental_read_options,
+ **{"hoodie.datasource.read.end.instanttime": endTime,
+ "hoodie.datasource.read.begin.instanttime": beginTime}}
Review comment:
hi @EdwinGuo, syntax error here, please use
```
point_in_time_read_options = {
'hoodie.datasource.query.type': 'incremental',
'hoodie.datasource.read.end.instanttime': endTime,
'hoodie.datasource.read.begin.instanttime': beginTime
}
```
##########
File path: docs/_docs/1_1_quick_start_guide.md
##########
@@ -204,6 +213,224 @@ spark.sql("select uuid, partitionPath from hudi_trips_snapshot").count()
```
Note: Only `Append` mode is supported for delete operation.
+# Pyspark example
+## Setup
+
+Hudi works with Spark-2.x versions. You can follow instructions [here](https://spark.apache.org/downloads.html) for setting up spark.
+From the extracted directory run spark-shell with Hudi as:
+
+```python
+# pyspark
+export PYSPARK_PYTHON=$(which python3)
+spark-2.4.4-bin-hadoop2.7/bin/pyspark \
+ --packages org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4 \
+ --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
+```
+
+<div class="notice--info">
+ <h4>Please note the following: </h4>
+<ul>
+ <li>spark-avro module needs to be specified in --packages as it is not included with spark-shell by default</li>
+ <li>spark-avro and spark versions must match (we have used 2.4.4 for both above)</li>
+ <li>we have used hudi-spark-bundle built for scala 2.11 since the spark-avro module used also depends on 2.11.
+ If spark-avro_2.12 is used, correspondingly hudi-spark-bundle_2.12 needs to be used. </li>
+</ul>
+</div>
+
+Setup table name, base path and a data generator to generate records for this guide.
+
+```python
+# pyspark
+tableName = "hudi_trips_cow"
+basePath = "file:///tmp/hudi_trips_cow"
+dataGen = sc._jvm.org.apache.hudi.QuickstartUtils.DataGenerator()
+```
+
+The [DataGenerator](https://github.com/apache/incubator-hudi/blob/master/hudi-spark/src/main/java/org/apache/hudi/QuickstartUtils.java#L50)
+can generate sample inserts and updates based on the the sample trip schema [here](https://github.com/apache/incubator-hudi/blob/master/hudi-spark/src/main/java/org/apache/hudi/QuickstartUtils.java#L57)
+{: .notice--info}
+
+
+## Insert data
+
+Generate some new trips, load them into a DataFrame and write the DataFrame into the Hudi table as below.
+
+```python
+# pyspark
+inserts = sc._jvm.org.apache.hudi.QuickstartUtils.convertToStringList(dataGen.generateInserts(10))
+df = spark.read.json(spark.sparkContext.parallelize(inserts, 2))
+
+hudi_options = {
+ 'hoodie.table.name': tableName,
+ 'hoodie.datasource.write.recordkey.field': 'uuid',
+ 'hoodie.datasource.write.partitionpath.field': 'partitionpath',
+ 'hoodie.datasource.write.table.name': tableName,
+ 'hoodie.datasource.write.operation': 'insert',
+ 'hoodie.datasource.write.precombine.field': 'ts',
+ 'hoodie.upsert.shuffle.parallelism': 2,
+ 'hoodie.insert.shuffle.parallelism': 2
+}
+
+df.write.format("hudi"). \
+ options(**hudi_options). \
+ mode("overwrite"). \
+ save(basePath)
+```
+
+`mode(Overwrite)` overwrites and recreates the table if it already exists.
+You can check the data generated under `/tmp/hudi_trips_cow/<region>/<country>/<city>/`. We provided a record key
+(`uuid` in [schema](https://github.com/apache/incubator-hudi/blob/master/hudi-spark/src/main/java/org/apache/hudi/QuickstartUtils.java#L58)), partition field (`region/county/city`) and combine logic (`ts` in
+[schema](https://github.com/apache/incubator-hudi/blob/master/hudi-spark/src/main/java/org/apache/hudi/QuickstartUtils.java#L58)) to ensure trip records are unique within each partition. For more info, refer to
+[Modeling data stored in Hudi](https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=113709185#FAQ-HowdoImodelthedatastoredinHudi)
+and for info on ways to ingest data into Hudi, refer to [Writing Hudi Tables](/docs/writing_data.html).
+Here we are using the default write operation : `upsert`. If you have a workload without updates, you can also issue
+`insert` or `bulk_insert` operations which could be faster. To know more, refer to [Write operations](/docs/writing_data#write-operations)
+{: .notice--info}
+
+## Query data
+
+Load the data files into a DataFrame.
+
+```python
+# pyspark
+tripsSnapshotDF = spark. \
+ read. \
+ format("hudi"). \
+ load(basePath + "/*/*/*/*")
+
+tripsSnapshotDF.createOrReplaceTempView("hudi_trips_snapshot")
+
+spark.sql("select fare, begin_lon, begin_lat, ts from hudi_trips_snapshot where fare > 20.0").show()
+spark.sql("select _hoodie_commit_time, _hoodie_record_key, _hoodie_partition_path, rider, driver, fare from hudi_trips_snapshot").show()
+```
+
+This query provides snapshot querying of the ingested data. Since our partition path (`region/country/city`) is 3 levels nested
+from base path we ve used `load(basePath + "/*/*/*/*")`.
+Refer to [Table types and queries](/docs/concepts#table-types--queries) for more info on all table types and query types supported.
+{: .notice--info}
+
+## Update data
+
+This is similar to inserting new data. Generate updates to existing trips using the data generator, load into a DataFrame
+and write DataFrame into the hudi table.
+
+```python
+# pyspark
+updates = sc._jvm.org.apache.hudi.QuickstartUtils.convertToStringList(dataGen.generateUpdates(10))
+df = spark.read.json(spark.sparkContext.parallelize(updates, 2))
+df.write.format("hudi"). \
+ options(**hudi_options). \
+ mode("append"). \
+ save(basePath)
+```
+
+Notice that the save mode is now `Append`. In general, always use append mode unless you are trying to create the table for the first time.
+[Querying](#query-data) the data again will now show updated trips. Each write operation generates a new [commit](http://hudi.incubator.apache.org/docs/concepts.html)
+denoted by the timestamp. Look for changes in `_hoodie_commit_time`, `rider`, `driver` fields for the same `_hoodie_record_key`s in previous commit.
+{: .notice--info}
+
+## Incremental query
+
+Hudi also provides capability to obtain a stream of records that changed since given commit timestamp.
+This can be achieved using Hudi's incremental querying and providing a begin time from which changes need to be streamed.
+We do not need to specify endTime, if we want all changes after the given commit (as is the common case).
+
+```python
+# pyspark
+# reload data
+spark. \
+ read. \
+ format("hudi"). \
+ load(basePath + "/*/*/*/*"). \
+ createOrReplaceTempView("hudi_trips_snapshot")
+
+commits = list(map(lambda row: row[0], spark.sql("select distinct(_hoodie_commit_time) as commitTime from hudi_trips_snapshot order by commitTime").limit(50).collect()))
+beginTime = commits[len(commits) - 2] # commit time we are interested in
+
+# incrementally query data
+incremental_read_options = {
+ 'hoodie.datasource.query.type': 'incremental',
+ 'hoodie.datasource.read.begin.instanttime': beginTime,
+}
+
+tripsIncrementalDF = spark.read.format("hudi"). \
+ options(**incremental_read_options). \
+ load(basePath)
+tripsIncrementalDF.createOrReplaceTempView("hudi_trips_incremental")
+
+spark.sql("select `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts from hudi_trips_incremental where fare > 20.0").show()
+```
+
+This will give all changes that happened after the beginTime commit with the filter of fare > 20.0. The unique thing about this
+feature is that it now lets you author streaming pipelines on batch data.
+{: .notice--info}
+
+## Point in time query
+
+Lets look at how to query data as of a specific time. The specific time can be represented by pointing endTime to a
+specific commit time and beginTime to "000" (denoting earliest possible commit time).
+
+```python
+# pyspark
+beginTime = "000" # Represents all commits > this time.
+endTime = commits[len(commits) - 2]
+
+# query point in time data
+point_in_time_read_options = {**incremental_read_options,
+ **{"hoodie.datasource.read.end.instanttime": endTime,
+ "hoodie.datasource.read.begin.instanttime": beginTime}}
+tripsPointInTimeDF = spark.read.format("hudi"). \
+ options(**point_in_time_read_options). \
+ load(basePath)
+
+tripsPointInTimeDF.createOrReplaceTempView("hudi_trips_point_in_time")
+spark.sql("select `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts from hudi_trips_point_in_time where fare > 20.0").show()
+```
+
+## Delete data {#deletes}
+Delete records for the HoodieKeys passed in.
+
+Note: Only `Append` mode is supported for delete operation.
+
+```python
+# pyspark
+# fetch total records count
+spark.sql("select uuid, partitionPath from hudi_trips_snapshot").count()
+# fetch two records to be deleted
+ds = spark.sql("select uuid, partitionPath from hudi_trips_snapshot").limit(2)
+
+# issue deletes
+hudi_delete_options = {
+'hoodie.table.name': tableName,
+'hoodie.datasource.write.recordkey.field': 'uuid',
+'hoodie.datasource.write.partitionpath.field': 'partitionpath',
+'hoodie.datasource.write.table.name': tableName,
+'hoodie.datasource.write.operation': 'delete',
+'hoodie.datasource.write.precombine.field': 'ts',
+'hoodie.upsert.shuffle.parallelism': 2,
+'hoodie.insert.shuffle.parallelism': 2
Review comment:
here, need two blank space.
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