MAPREDUCE-7403. manifest-committer dynamic partitioning support. (apa…

…che#4728)


Declares its compatibility with Spark's dynamic
output partitioning by having the stream capability
"mapreduce.job.committer.dynamic.partitioning"

Requires a Spark release with SPARK-40034, which
does the probing before deciding whether to 
accept/rejecting instantiation with
dynamic partition overwrite set

This feature can be declared as supported by
any other PathOutputCommitter implementations
whose algorithm and destination filesystem
are compatible.

None of the S3A committers are compatible.

The classic FileOutputCommitter is, but it
does not declare itself as such out of our fear
of changing that code. The Spark-side code
will automatically infer compatibility if
the created committer is of that class or
a subclass.

Contributed by Steve Loughran.

hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/lib/output/BindingPathOutputCommitter.java

@@ -23,6 +23,10 @@
 import org.apache.hadoop.classification.InterfaceAudience;
 import org.apache.hadoop.classification.InterfaceStability;
 import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.fs.StreamCapabilities;
+import org.apache.hadoop.fs.statistics.IOStatistics;
+import org.apache.hadoop.fs.statistics.IOStatisticsSource;
+import org.apache.hadoop.fs.statistics.IOStatisticsSupport;
 import org.apache.hadoop.mapreduce.JobContext;
 import org.apache.hadoop.mapreduce.JobStatus;
 import org.apache.hadoop.mapreduce.TaskAttemptContext;
@@ -60,7 +64,8 @@
  */
 @InterfaceAudience.Public
 @InterfaceStability.Unstable
-public class BindingPathOutputCommitter extends PathOutputCommitter {
+public class BindingPathOutputCommitter extends PathOutputCommitter
+    implements IOStatisticsSource, StreamCapabilities {
 
   /**
    * The classname for use in configurations.
@@ -181,4 +186,22 @@ public String toString() {
   public PathOutputCommitter getCommitter() {
     return committer;
   }
+
+  /**
+   * Pass through if the inner committer supports StreamCapabilities.
+   * {@inheritDoc}
+   */
+  @Override
+  public boolean hasCapability(final String capability) {
+    if (committer instanceof StreamCapabilities) {
+      return ((StreamCapabilities) committer).hasCapability(capability);
+    } else {
+      return false;
+    }
+  }
+
+  @Override
+  public IOStatistics getIOStatistics() {
+    return IOStatisticsSupport.retrieveIOStatistics(committer);
+  }
 }

hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/lib/output/committer/manifest/ManifestCommitter.java

@@ -31,6 +31,7 @@
 import org.apache.hadoop.fs.FileStatus;
 import org.apache.hadoop.fs.FileSystem;
 import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.fs.StreamCapabilities;
 import org.apache.hadoop.fs.statistics.IOStatisticsSource;
 import org.apache.hadoop.fs.statistics.impl.IOStatisticsStore;
 import org.apache.hadoop.mapreduce.JobContext;
@@ -55,6 +56,7 @@
 
 import static org.apache.hadoop.fs.statistics.IOStatisticsLogging.ioStatisticsToPrettyString;
 import static org.apache.hadoop.fs.statistics.IOStatisticsLogging.logIOStatisticsAtDebug;
+import static org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterConstants.CAPABILITY_DYNAMIC_PARTITIONING;
 import static org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterConstants.OPT_DIAGNOSTICS_MANIFEST_DIR;
 import static org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterConstants.OPT_SUMMARY_REPORT_DIR;
 import static org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterStatisticNames.COMMITTER_TASKS_COMPLETED_COUNT;
@@ -84,7 +86,7 @@
 @InterfaceAudience.Public
 @InterfaceStability.Stable
 public class ManifestCommitter extends PathOutputCommitter implements
-    IOStatisticsSource, StageEventCallbacks {
+    IOStatisticsSource, StageEventCallbacks, StreamCapabilities {
 
   public static final Logger LOG = LoggerFactory.getLogger(
       ManifestCommitter.class);
@@ -758,4 +760,15 @@ private static Path maybeSaveSummary(
   public IOStatisticsStore getIOStatistics() {
     return iostatistics;
   }
+
+  /**
+   * The committer is compatible with spark's dynamic partitioning
+   * algorithm.
+   * @param capability string to query the stream support for.
+   * @return true if the requested capability is supported.
+   */
+  @Override
+  public boolean hasCapability(final String capability) {
+    return CAPABILITY_DYNAMIC_PARTITIONING.equals(capability);
+  }
 }

hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/lib/output/committer/manifest/ManifestCommitterConstants.java

@@ -234,6 +234,12 @@ public final class ManifestCommitterConstants {
    */
   public static final String CONTEXT_ATTR_TASK_ATTEMPT_ID = "ta";
 
+  /**
+   * Stream Capabilities probe for spark dynamic partitioning compatibility.
+   */
+  public static final String CAPABILITY_DYNAMIC_PARTITIONING =
+      "mapreduce.job.committer.dynamic.partitioning";
+
   private ManifestCommitterConstants() {
   }
 

hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/site/markdown/manifest_committer.md

@@ -269,6 +269,76 @@ appending data are creating and writing into new partitions.
 job to create unique files. This is foundational for
 any job to generate correct data.
 
+# <a name="dynamic"></a> Spark Dynamic Partition overwriting
+
+Spark has a feature called "Dynamic Partition Overwrites",
+
+This can be initiated in SQL
+```SQL
+INSERT OVERWRITE TABLE ...
+```
+Or through DataSet writes where the mode is `overwrite` and the partitioning matches
+that of the existing table
+```scala
+sparkConf.set("spark.sql.sources.partitionOverwriteMode", "dynamic")
+// followed by an overwrite of a Dataset into an existing partitioned table.
+eventData2
+  .write
+  .mode("overwrite")
+  .partitionBy("year", "month")
+  .format("parquet")
+  .save(existingDir)
+```
+
+This feature is implemented in Spark, which
+1. Directs the job to write its new data to a temporary directory
+1. After job commit completes, scans the output to identify the leaf directories "partitions" into which data was written.
+1. Deletes the content of those directories in the destination table
+1. Renames the new files into the partitions.
+
+This is all done in spark, which takes over the tasks of scanning
+the intermediate output tree, deleting partitions and of
+renaming the new files.
+
+This feature also adds the ability for a job to write data entirely outside
+the destination table, which is done by
+1. writing new files into the working directory
+1. spark moving them to the final destination in job commit
+
+
+The manifest committer is compatible with dynamic partition overwrites
+on Azure and Google cloud storage as together they meet the core requirements of
+the extension:
+1. The working directory returned in `getWorkPath()` is in the same filesystem
+  as the final output.
+2. `rename()` is an `O(1)` operation which is safe and fast to use when committing a job.
+
+None of the S3A committers support this. Condition (1) is not met by
+the staging committers, while (2) is not met by S3 itself.
+
+To use the manifest committer with dynamic partition overwrites, the
+spark version must contain
+[SPARK-40034](https://issues.apache.org/jira/browse/SPARK-40034)
+_PathOutputCommitters to work with dynamic partition overwrite_.
+
+Be aware that the rename phase of the operation will be slow
+if many files are renamed -this is done sequentially.
+Parallel renaming would speed this up, *but could trigger the abfs overload
+problems the manifest committer is designed to both minimize the risk
+of and support recovery from*
+
+The spark side of the commit operation will be listing/treewalking
+the temporary output directory (some overhead), followed by
+the file promotion, done with a classic filesystem `rename()`
+call. There will be no explicit rate limiting here.
+
+*What does this mean?*
+
+It means that _dynamic partitioning should not be used on Azure Storage
+for SQL queries/Spark DataSet operations where many thousands of files are created.
+The fact that these will suffer from performance problems before
+throttling scale issues surface, should be considered a warning.
+
 # <a name="SUCCESS"></a> Job Summaries in `_SUCCESS` files
 
 The original hadoop committer creates a zero byte `_SUCCESS` file in the root of the output directory
@@ -585,7 +655,7 @@ There is no need to alter these values, except when writing new implementations
 something which is only needed if the store provides extra integration support for the
 committer.
 
-## <a name="concurrent"></a> Support for concurrent test runs.
+## <a name="concurrent"></a> Support for concurrent jobs to the same directory
 
 It *may* be possible to run multiple jobs targeting the same directory tree.
 
@@ -600,6 +670,8 @@ For this to work, a number of conditions must be met:
   `mapreduce.fileoutputcommitter.cleanup.skipped` to `true`.
 * All jobs/tasks must create files with unique filenames.
 * All jobs must create output with the same directory partition structure.
+* The job/queries MUST NOT be using Spark Dynamic Partitioning "INSERT OVERWRITE TABLE"; data may be lost.
+  This holds for *all* committers, not just the manifest committer.
 * Remember to delete the `_temporary` directory later!
 
 This has *NOT BEEN TESTED*

hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/test/java/org/apache/hadoop/mapreduce/lib/output/committer/manifest/TestManifestCommitProtocol.java

@@ -61,6 +61,7 @@
 import org.apache.hadoop.mapreduce.RecordWriter;
 import org.apache.hadoop.mapreduce.TaskAttemptContext;
 import org.apache.hadoop.mapreduce.TaskAttemptID;
+import org.apache.hadoop.mapreduce.lib.output.BindingPathOutputCommitter;
 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
 import org.apache.hadoop.mapreduce.lib.output.MapFileOutputFormat;
 import org.apache.hadoop.mapreduce.lib.output.PathOutputCommitterFactory;
@@ -1549,6 +1550,23 @@ public void testOutputFormatIntegration() throws Throwable {
     ManifestCommitter committer = (ManifestCommitter)
         outputFormat.getOutputCommitter(tContext);
 
+    // check path capabilities directly
+    Assertions.assertThat(committer.hasCapability(
+            ManifestCommitterConstants.CAPABILITY_DYNAMIC_PARTITIONING))
+        .describedAs("dynamic partitioning capability in committer %s",
+            committer)
+        .isTrue();
+    // and through a binding committer -passthrough is critical
+    // for the spark binding.
+    BindingPathOutputCommitter bindingCommitter =
+        new BindingPathOutputCommitter(outputDir, tContext);
+    Assertions.assertThat(bindingCommitter.hasCapability(
+            ManifestCommitterConstants.CAPABILITY_DYNAMIC_PARTITIONING))
+        .describedAs("dynamic partitioning capability in committer %s",
+            bindingCommitter)
+        .isTrue();
+
+
     // setup
     JobData jobData = new JobData(job, jContext, tContext, committer);
     setupJob(jobData);