[jira] [Updated] (HUDI-625) Address performance concerns on DiskBasedMap.get() during upsert of thin records
[
https://issues.apache.org/jira/browse/HUDI-625?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Vinoth Chandar updated HUDI-625:
Status: Open (was: New)
> Address performance concerns on DiskBasedMap.get() during upsert of thin
> records
>
>
> Key: HUDI-625
> URL: https://issues.apache.org/jira/browse/HUDI-625
> Project: Apache Hudi (incubating)
> Issue Type: Improvement
> Components: Performance, Writer Core
>Reporter: Vinoth Chandar
>Assignee: lamber-ken
>Priority: Major
> Labels: pull-request-available
> Fix For: 0.5.2
>
> Attachments: image-2020-02-20-23-34-24-155.png,
> image-2020-02-20-23-34-27-466.png, image-2020-02-21-15-35-56-637.png,
> image-2020-02-24-08-15-48-615.png, image-2020-02-24-08-17-33-739.png
>
> Time Spent: 0.5h
> Remaining Estimate: 0h
>
> [https://github.com/apache/incubator-hudi/issues/1328]
>
> So what's going on here is that each entry (single data field) is estimated
> to be around 500-750 bytes in memory and things spill a lot...
> {code:java}
> 20/02/20 23:00:39 INFO ExternalSpillableMap: Estimated Payload size => 760
> for 3675605,HoodieRecord{key=HoodieKey { recordKey=3675605
> partitionPath=default}, currentLocation='HoodieRecordLocation
> {instantTime=20200220225748, fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}',
> newLocation='HoodieRecordLocation {instantTime=20200220225921,
> fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}'} {code}
>
> {code:java}
> INFO HoodieMergeHandle: Number of entries in MemoryBasedMap => 150875
> Total size in bytes of MemoryBasedMap => 83886580
> Number of entries in DiskBasedMap => 2849125
> Size of file spilled to disk => 1067101739 {code}
> h2. Reproduce steps
>
> {code:java}
> export SPARK_HOME=/home/dockeradmin/hudi/spark-2.4.4-bin-hadoop2.7
> ${SPARK_HOME}/bin/spark-shell \
> --executor-memory 6G \
> --packages
> org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4
> \
> --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
> {code}
>
> {code:java}
> val HUDI_FORMAT = "org.apache.hudi"
> val TABLE_NAME = "hoodie.table.name"
> val RECORDKEY_FIELD_OPT_KEY = "hoodie.datasource.write.recordkey.field"
> val PRECOMBINE_FIELD_OPT_KEY = "hoodie.datasource.write.precombine.field"
> val OPERATION_OPT_KEY = "hoodie.datasource.write.operation"
> val BULK_INSERT_OPERATION_OPT_VAL = "bulk_insert"
> val UPSERT_OPERATION_OPT_VAL = "upsert"
> val BULK_INSERT_PARALLELISM = "hoodie.bulkinsert.shuffle.parallelism"
> val UPSERT_PARALLELISM = "hoodie.upsert.shuffle.parallelism"
> val config = Map(
> "table_name" -> "example_table",
> "target" -> "file:///tmp/example_table/",
> "primary_key" -> "id",
> "sort_key" -> "id"
> )
> val readPath = config("target") + "/*"val json_data = (1 to 400).map(i =>
> "{\"id\":" + i + "}")
> val jsonRDD = spark.sparkContext.parallelize(json_data, 2)
> val df1 = spark.read.json(jsonRDD)
> println(s"${df1.count()} records in source 1")
> df1.write.format(HUDI_FORMAT).
> option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
> option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
> option(TABLE_NAME, config("table_name")).
> option(OPERATION_OPT_KEY, BULK_INSERT_OPERATION_OPT_VAL).
> option(BULK_INSERT_PARALLELISM, 1).
> mode("Overwrite").
>
> save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
> records in Hudi table")
> // Runs very slow
> df1.limit(300).write.format(HUDI_FORMAT).
> option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
> option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
> option(TABLE_NAME, config("table_name")).
> option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
> option(UPSERT_PARALLELISM, 20).
> mode("Append").
> save(config("target"))
> // Runs very slow
> df1.write.format(HUDI_FORMAT).
> option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
> option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
> option(TABLE_NAME, config("table_name")).
> option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
> option(UPSERT_PARALLELISM, 20).
> mode("Append").
>
> save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
> records in Hudi table")
> {code}
>
>
>
> h2. *Analysis*
> h3. *Upsert (400 entries)*
> {code:java}
> WARN HoodieMergeHandle:
> Number of entries in MemoryBasedMap => 150875
> Total size in bytes of MemoryBasedMap => 83886580
> Number of entries in DiskBasedMap => 3849125
> Size of file spilled to disk => 1443046132
> {code}
> h3. Hang stackstrace (DiskBasedMap#get)
>
> {code:java}
> "pool-21-thread-2" Id=696 cpuUsage=98% RUNNABLE
> at java.util.zip.ZipFile.getEntry(Native
[jira] [Updated] (HUDI-625) Address performance concerns on DiskBasedMap.get() during upsert of thin records
[
https://issues.apache.org/jira/browse/HUDI-625?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Vinoth Chandar updated HUDI-625:
Fix Version/s: (was: 0.6.0)
0.5.2
> Address performance concerns on DiskBasedMap.get() during upsert of thin
> records
>
>
> Key: HUDI-625
> URL: https://issues.apache.org/jira/browse/HUDI-625
> Project: Apache Hudi (incubating)
> Issue Type: Improvement
> Components: Performance, Writer Core
>Reporter: Vinoth Chandar
>Assignee: lamber-ken
>Priority: Major
> Labels: pull-request-available
> Fix For: 0.5.2
>
> Attachments: image-2020-02-20-23-34-24-155.png,
> image-2020-02-20-23-34-27-466.png, image-2020-02-21-15-35-56-637.png,
> image-2020-02-24-08-15-48-615.png, image-2020-02-24-08-17-33-739.png
>
> Time Spent: 0.5h
> Remaining Estimate: 0h
>
> [https://github.com/apache/incubator-hudi/issues/1328]
>
> So what's going on here is that each entry (single data field) is estimated
> to be around 500-750 bytes in memory and things spill a lot...
> {code:java}
> 20/02/20 23:00:39 INFO ExternalSpillableMap: Estimated Payload size => 760
> for 3675605,HoodieRecord{key=HoodieKey { recordKey=3675605
> partitionPath=default}, currentLocation='HoodieRecordLocation
> {instantTime=20200220225748, fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}',
> newLocation='HoodieRecordLocation {instantTime=20200220225921,
> fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}'} {code}
>
> {code:java}
> INFO HoodieMergeHandle: Number of entries in MemoryBasedMap => 150875
> Total size in bytes of MemoryBasedMap => 83886580
> Number of entries in DiskBasedMap => 2849125
> Size of file spilled to disk => 1067101739 {code}
> h2. Reproduce steps
>
> {code:java}
> export SPARK_HOME=/home/dockeradmin/hudi/spark-2.4.4-bin-hadoop2.7
> ${SPARK_HOME}/bin/spark-shell \
> --executor-memory 6G \
> --packages
> org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4
> \
> --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
> {code}
>
> {code:java}
> val HUDI_FORMAT = "org.apache.hudi"
> val TABLE_NAME = "hoodie.table.name"
> val RECORDKEY_FIELD_OPT_KEY = "hoodie.datasource.write.recordkey.field"
> val PRECOMBINE_FIELD_OPT_KEY = "hoodie.datasource.write.precombine.field"
> val OPERATION_OPT_KEY = "hoodie.datasource.write.operation"
> val BULK_INSERT_OPERATION_OPT_VAL = "bulk_insert"
> val UPSERT_OPERATION_OPT_VAL = "upsert"
> val BULK_INSERT_PARALLELISM = "hoodie.bulkinsert.shuffle.parallelism"
> val UPSERT_PARALLELISM = "hoodie.upsert.shuffle.parallelism"
> val config = Map(
> "table_name" -> "example_table",
> "target" -> "file:///tmp/example_table/",
> "primary_key" -> "id",
> "sort_key" -> "id"
> )
> val readPath = config("target") + "/*"val json_data = (1 to 400).map(i =>
> "{\"id\":" + i + "}")
> val jsonRDD = spark.sparkContext.parallelize(json_data, 2)
> val df1 = spark.read.json(jsonRDD)
> println(s"${df1.count()} records in source 1")
> df1.write.format(HUDI_FORMAT).
> option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
> option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
> option(TABLE_NAME, config("table_name")).
> option(OPERATION_OPT_KEY, BULK_INSERT_OPERATION_OPT_VAL).
> option(BULK_INSERT_PARALLELISM, 1).
> mode("Overwrite").
>
> save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
> records in Hudi table")
> // Runs very slow
> df1.limit(300).write.format(HUDI_FORMAT).
> option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
> option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
> option(TABLE_NAME, config("table_name")).
> option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
> option(UPSERT_PARALLELISM, 20).
> mode("Append").
> save(config("target"))
> // Runs very slow
> df1.write.format(HUDI_FORMAT).
> option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
> option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
> option(TABLE_NAME, config("table_name")).
> option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
> option(UPSERT_PARALLELISM, 20).
> mode("Append").
>
> save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
> records in Hudi table")
> {code}
>
>
>
> h2. *Analysis*
> h3. *Upsert (400 entries)*
> {code:java}
> WARN HoodieMergeHandle:
> Number of entries in MemoryBasedMap => 150875
> Total size in bytes of MemoryBasedMap => 83886580
> Number of entries in DiskBasedMap => 3849125
> Size of file spilled to disk => 1443046132
> {code}
> h3. Hang stackstrace (DiskBasedMap#get)
>
> {code:java}
> "pool-21-thread-2" Id=696 cpuUsage=98% RUNNABLE
> at
[jira] [Updated] (HUDI-625) Address performance concerns on DiskBasedMap.get() during upsert of thin records
[
https://issues.apache.org/jira/browse/HUDI-625?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
lamber-ken updated HUDI-625:
Attachment: image-2020-02-24-08-15-48-615.png
> Address performance concerns on DiskBasedMap.get() during upsert of thin
> records
>
>
> Key: HUDI-625
> URL: https://issues.apache.org/jira/browse/HUDI-625
> Project: Apache Hudi (incubating)
> Issue Type: Improvement
> Components: Performance, Writer Core
>Reporter: Vinoth Chandar
>Assignee: Vinoth Chandar
>Priority: Major
> Labels: pull-request-available
> Fix For: 0.6.0
>
> Attachments: image-2020-02-20-23-34-24-155.png,
> image-2020-02-20-23-34-27-466.png, image-2020-02-21-15-35-56-637.png,
> image-2020-02-24-08-15-48-615.png
>
> Time Spent: 10m
> Remaining Estimate: 0h
>
> [https://github.com/apache/incubator-hudi/issues/1328]
>
> So what's going on here is that each entry (single data field) is estimated
> to be around 500-750 bytes in memory and things spill a lot...
> {code:java}
> 20/02/20 23:00:39 INFO ExternalSpillableMap: Estimated Payload size => 760
> for 3675605,HoodieRecord{key=HoodieKey { recordKey=3675605
> partitionPath=default}, currentLocation='HoodieRecordLocation
> {instantTime=20200220225748, fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}',
> newLocation='HoodieRecordLocation {instantTime=20200220225921,
> fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}'} {code}
>
> {code:java}
> INFO HoodieMergeHandle: Number of entries in MemoryBasedMap => 150875
> Total size in bytes of MemoryBasedMap => 83886580
> Number of entries in DiskBasedMap => 2849125
> Size of file spilled to disk => 1067101739 {code}
> h2. Reproduce steps
>
> {code:java}
> export SPARK_HOME=/home/dockeradmin/hudi/spark-2.4.4-bin-hadoop2.7
> ${SPARK_HOME}/bin/spark-shell \
> --executor-memory 6G \
> --packages
> org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4
> \
> --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
> {code}
>
> {code:java}
> val HUDI_FORMAT = "org.apache.hudi"
> val TABLE_NAME = "hoodie.table.name"
> val RECORDKEY_FIELD_OPT_KEY = "hoodie.datasource.write.recordkey.field"
> val PRECOMBINE_FIELD_OPT_KEY = "hoodie.datasource.write.precombine.field"
> val OPERATION_OPT_KEY = "hoodie.datasource.write.operation"
> val BULK_INSERT_OPERATION_OPT_VAL = "bulk_insert"
> val UPSERT_OPERATION_OPT_VAL = "upsert"
> val BULK_INSERT_PARALLELISM = "hoodie.bulkinsert.shuffle.parallelism"
> val UPSERT_PARALLELISM = "hoodie.upsert.shuffle.parallelism"
> val config = Map(
> "table_name" -> "example_table",
> "target" -> "file:///tmp/example_table/",
> "primary_key" -> "id",
> "sort_key" -> "id"
> )
> val readPath = config("target") + "/*"val json_data = (1 to 400).map(i =>
> "{\"id\":" + i + "}")
> val jsonRDD = spark.sparkContext.parallelize(json_data, 2)
> val df1 = spark.read.json(jsonRDD)
> println(s"${df1.count()} records in source 1")
> df1.write.format(HUDI_FORMAT).
> option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
> option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
> option(TABLE_NAME, config("table_name")).
> option(OPERATION_OPT_KEY, BULK_INSERT_OPERATION_OPT_VAL).
> option(BULK_INSERT_PARALLELISM, 1).
> mode("Overwrite").
>
> save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
> records in Hudi table")
> // Runs very slow
> df1.limit(300).write.format(HUDI_FORMAT).
> option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
> option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
> option(TABLE_NAME, config("table_name")).
> option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
> option(UPSERT_PARALLELISM, 20).
> mode("Append").
> save(config("target"))
> // Runs very slow
> df1.write.format(HUDI_FORMAT).
> option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
> option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
> option(TABLE_NAME, config("table_name")).
> option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
> option(UPSERT_PARALLELISM, 20).
> mode("Append").
>
> save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
> records in Hudi table")
> {code}
>
>
>
> h2. *Analysis*
> h3. *Upsert (400 entries)*
> {code:java}
> WARN HoodieMergeHandle:
> Number of entries in MemoryBasedMap => 150875
> Total size in bytes of MemoryBasedMap => 83886580
> Number of entries in DiskBasedMap => 3849125
> Size of file spilled to disk => 1443046132
> {code}
> h3. Hang stackstrace (DiskBasedMap#get)
>
> {code:java}
> "pool-21-thread-2" Id=696 cpuUsage=98% RUNNABLE
> at java.util.zip.ZipFile.getEntry(Native Method)
> at
[jira] [Updated] (HUDI-625) Address performance concerns on DiskBasedMap.get() during upsert of thin records
[
https://issues.apache.org/jira/browse/HUDI-625?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
ASF GitHub Bot updated HUDI-625:
Labels: pull-request-available (was: )
> Address performance concerns on DiskBasedMap.get() during upsert of thin
> records
>
>
> Key: HUDI-625
> URL: https://issues.apache.org/jira/browse/HUDI-625
> Project: Apache Hudi (incubating)
> Issue Type: Improvement
> Components: Performance, Writer Core
>Reporter: Vinoth Chandar
>Assignee: Vinoth Chandar
>Priority: Major
> Labels: pull-request-available
> Fix For: 0.6.0
>
> Attachments: image-2020-02-20-23-34-24-155.png,
> image-2020-02-20-23-34-27-466.png, image-2020-02-21-15-35-56-637.png
>
>
> [https://github.com/apache/incubator-hudi/issues/1328]
>
> So what's going on here is that each entry (single data field) is estimated
> to be around 500-750 bytes in memory and things spill a lot...
> {code:java}
> 20/02/20 23:00:39 INFO ExternalSpillableMap: Estimated Payload size => 760
> for 3675605,HoodieRecord{key=HoodieKey { recordKey=3675605
> partitionPath=default}, currentLocation='HoodieRecordLocation
> {instantTime=20200220225748, fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}',
> newLocation='HoodieRecordLocation {instantTime=20200220225921,
> fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}'} {code}
>
> {code:java}
> INFO HoodieMergeHandle: Number of entries in MemoryBasedMap => 150875
> Total size in bytes of MemoryBasedMap => 83886580
> Number of entries in DiskBasedMap => 2849125
> Size of file spilled to disk => 1067101739 {code}
> h2. Reproduce steps
>
> {code:java}
> export SPARK_HOME=/home/dockeradmin/hudi/spark-2.4.4-bin-hadoop2.7
> ${SPARK_HOME}/bin/spark-shell \
> --executor-memory 6G \
> --packages
> org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4
> \
> --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
> {code}
>
> {code:java}
> val HUDI_FORMAT = "org.apache.hudi"
> val TABLE_NAME = "hoodie.table.name"
> val RECORDKEY_FIELD_OPT_KEY = "hoodie.datasource.write.recordkey.field"
> val PRECOMBINE_FIELD_OPT_KEY = "hoodie.datasource.write.precombine.field"
> val OPERATION_OPT_KEY = "hoodie.datasource.write.operation"
> val BULK_INSERT_OPERATION_OPT_VAL = "bulk_insert"
> val UPSERT_OPERATION_OPT_VAL = "upsert"
> val BULK_INSERT_PARALLELISM = "hoodie.bulkinsert.shuffle.parallelism"
> val UPSERT_PARALLELISM = "hoodie.upsert.shuffle.parallelism"
> val config = Map(
> "table_name" -> "example_table",
> "target" -> "file:///tmp/example_table/",
> "primary_key" -> "id",
> "sort_key" -> "id"
> )
> val readPath = config("target") + "/*"val json_data = (1 to 400).map(i =>
> "{\"id\":" + i + "}")
> val jsonRDD = spark.sparkContext.parallelize(json_data, 2)
> val df1 = spark.read.json(jsonRDD)
> println(s"${df1.count()} records in source 1")
> df1.write.format(HUDI_FORMAT).
> option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
> option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
> option(TABLE_NAME, config("table_name")).
> option(OPERATION_OPT_KEY, BULK_INSERT_OPERATION_OPT_VAL).
> option(BULK_INSERT_PARALLELISM, 1).
> mode("Overwrite").
>
> save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
> records in Hudi table")
> // Runs very slow
> df1.limit(300).write.format(HUDI_FORMAT).
> option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
> option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
> option(TABLE_NAME, config("table_name")).
> option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
> option(UPSERT_PARALLELISM, 20).
> mode("Append").
> save(config("target"))
> // Runs very slow
> df1.write.format(HUDI_FORMAT).
> option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
> option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
> option(TABLE_NAME, config("table_name")).
> option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
> option(UPSERT_PARALLELISM, 20).
> mode("Append").
>
> save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
> records in Hudi table")
> {code}
>
>
>
> h2. *Analysis*
> h3. *Upsert (400 entries)*
> {code:java}
> WARN HoodieMergeHandle:
> Number of entries in MemoryBasedMap => 150875
> Total size in bytes of MemoryBasedMap => 83886580
> Number of entries in DiskBasedMap => 3849125
> Size of file spilled to disk => 1443046132
> {code}
> h3. Hang stackstrace (DiskBasedMap#get)
>
> {code:java}
> "pool-21-thread-2" Id=696 cpuUsage=98% RUNNABLE
> at java.util.zip.ZipFile.getEntry(Native Method)
> at java.util.zip.ZipFile.getEntry(ZipFile.java:310)
> - locked
[jira] [Updated] (HUDI-625) Address performance concerns on DiskBasedMap.get() during upsert of thin records
[
https://issues.apache.org/jira/browse/HUDI-625?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Vinoth Chandar updated HUDI-625:
Description:
[https://github.com/apache/incubator-hudi/issues/1328]
So what's going on here is that each entry (single data field) is estimated to
be around 500-750 bytes in memory and things spill a lot...
{code:java}
20/02/20 23:00:39 INFO ExternalSpillableMap: Estimated Payload size => 760 for
3675605,HoodieRecord{key=HoodieKey { recordKey=3675605 partitionPath=default},
currentLocation='HoodieRecordLocation {instantTime=20200220225748,
fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}',
newLocation='HoodieRecordLocation {instantTime=20200220225921,
fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}'} {code}
{code:java}
INFO HoodieMergeHandle: Number of entries in MemoryBasedMap => 150875
Total size in bytes of MemoryBasedMap => 83886580
Number of entries in DiskBasedMap => 2849125
Size of file spilled to disk => 1067101739 {code}
h2. Reproduce steps
{code:java}
export SPARK_HOME=/home/dockeradmin/hudi/spark-2.4.4-bin-hadoop2.7
${SPARK_HOME}/bin/spark-shell \
--executor-memory 6G \
--packages
org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4
\
--conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
{code}
{code:java}
val HUDI_FORMAT = "org.apache.hudi"
val TABLE_NAME = "hoodie.table.name"
val RECORDKEY_FIELD_OPT_KEY = "hoodie.datasource.write.recordkey.field"
val PRECOMBINE_FIELD_OPT_KEY = "hoodie.datasource.write.precombine.field"
val OPERATION_OPT_KEY = "hoodie.datasource.write.operation"
val BULK_INSERT_OPERATION_OPT_VAL = "bulk_insert"
val UPSERT_OPERATION_OPT_VAL = "upsert"
val BULK_INSERT_PARALLELISM = "hoodie.bulkinsert.shuffle.parallelism"
val UPSERT_PARALLELISM = "hoodie.upsert.shuffle.parallelism"
val config = Map(
"table_name" -> "example_table",
"target" -> "file:///tmp/example_table/",
"primary_key" -> "id",
"sort_key" -> "id"
)
val readPath = config("target") + "/*"val json_data = (1 to 400).map(i =>
"{\"id\":" + i + "}")
val jsonRDD = spark.sparkContext.parallelize(json_data, 2)
val df1 = spark.read.json(jsonRDD)
println(s"${df1.count()} records in source 1")
df1.write.format(HUDI_FORMAT).
option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
option(TABLE_NAME, config("table_name")).
option(OPERATION_OPT_KEY, BULK_INSERT_OPERATION_OPT_VAL).
option(BULK_INSERT_PARALLELISM, 1).
mode("Overwrite").
save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
records in Hudi table")
// Runs very slow
df1.limit(300).write.format(HUDI_FORMAT).
option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
option(TABLE_NAME, config("table_name")).
option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
option(UPSERT_PARALLELISM, 20).
mode("Append").
save(config("target"))
// Runs very slow
df1.write.format(HUDI_FORMAT).
option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
option(TABLE_NAME, config("table_name")).
option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
option(UPSERT_PARALLELISM, 20).
mode("Append").
save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
records in Hudi table")
{code}
h2. *Analysis*
h3. *Upsert (400 entries)*
{code:java}
WARN HoodieMergeHandle:
Number of entries in MemoryBasedMap => 150875
Total size in bytes of MemoryBasedMap => 83886580
Number of entries in DiskBasedMap => 3849125
Size of file spilled to disk => 1443046132
{code}
h3. Hang stackstrace (DiskBasedMap#get)
{code:java}
"pool-21-thread-2" Id=696 cpuUsage=98% RUNNABLE
at java.util.zip.ZipFile.getEntry(Native Method)
at java.util.zip.ZipFile.getEntry(ZipFile.java:310)
- locked java.util.jar.JarFile@1fc27ed4
at java.util.jar.JarFile.getEntry(JarFile.java:240)
at java.util.jar.JarFile.getJarEntry(JarFile.java:223)
at sun.misc.URLClassPath$JarLoader.getResource(URLClassPath.java:1005)
at sun.misc.URLClassPath.getResource(URLClassPath.java:212)
at java.net.URLClassLoader$1.run(URLClassLoader.java:365)
at java.net.URLClassLoader$1.run(URLClassLoader.java:362)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:361)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
- locked java.lang.Object@28f65251
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331)
at java.lang.ClassLoader.loadClass(ClassLoader.java:411)
- locked
scala.reflect.internal.util.ScalaClassLoader$URLClassLoader@a353dff
at java.lang.ClassLoader.loadClass(ClassLoader.java:411)
- locked
[jira] [Updated] (HUDI-625) Address performance concerns on DiskBasedMap.get() during upsert of thin records
[
https://issues.apache.org/jira/browse/HUDI-625?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Vinoth Chandar updated HUDI-625:
Description:
[https://github.com/apache/incubator-hudi/issues/1328]
So what's going on here is that each entry (single data field) is estimated to
be around 500-750 bytes in memory and things spill a lot...
{code:java}
20/02/20 23:00:39 INFO ExternalSpillableMap: Estimated Payload size => 760 for
3675605,HoodieRecord{key=HoodieKey { recordKey=3675605 partitionPath=default},
currentLocation='HoodieRecordLocation {instantTime=20200220225748,
fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}',
newLocation='HoodieRecordLocation {instantTime=20200220225921,
fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}'} {code}
{code:java}
INFO HoodieMergeHandle: Number of entries in MemoryBasedMap => 150875Total size
in bytes of MemoryBasedMap => 83886580Number of entries in DiskBasedMap =>
2849125Size of file spilled to disk => 1067101739 {code}
h2. Reproduce steps
{code:java}
export SPARK_HOME=/home/dockeradmin/hudi/spark-2.4.4-bin-hadoop2.7
${SPARK_HOME}/bin/spark-shell \
--executor-memory 6G \
--packages
org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4
\
--conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
{code}
{code:java}
val HUDI_FORMAT = "org.apache.hudi"
val TABLE_NAME = "hoodie.table.name"
val RECORDKEY_FIELD_OPT_KEY = "hoodie.datasource.write.recordkey.field"
val PRECOMBINE_FIELD_OPT_KEY = "hoodie.datasource.write.precombine.field"
val OPERATION_OPT_KEY = "hoodie.datasource.write.operation"
val BULK_INSERT_OPERATION_OPT_VAL = "bulk_insert"
val UPSERT_OPERATION_OPT_VAL = "upsert"
val BULK_INSERT_PARALLELISM = "hoodie.bulkinsert.shuffle.parallelism"
val UPSERT_PARALLELISM = "hoodie.upsert.shuffle.parallelism"
val config = Map(
"table_name" -> "example_table",
"target" -> "file:///tmp/example_table/",
"primary_key" -> "id",
"sort_key" -> "id"
)
val readPath = config("target") + "/*"val json_data = (1 to 400).map(i =>
"{\"id\":" + i + "}")
val jsonRDD = spark.sparkContext.parallelize(json_data, 2)
val df1 = spark.read.json(jsonRDD)
println(s"${df1.count()} records in source 1")
df1.write.format(HUDI_FORMAT).
option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
option(TABLE_NAME, config("table_name")).
option(OPERATION_OPT_KEY, BULK_INSERT_OPERATION_OPT_VAL).
option(BULK_INSERT_PARALLELISM, 1).
mode("Overwrite").
save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
records in Hudi table")
// Runs very slow
df1.limit(300).write.format(HUDI_FORMAT).
option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
option(TABLE_NAME, config("table_name")).
option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
option(UPSERT_PARALLELISM, 20).
mode("Append").
save(config("target"))
// Runs very slow
df1.write.format(HUDI_FORMAT).
option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
option(TABLE_NAME, config("table_name")).
option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
option(UPSERT_PARALLELISM, 20).
mode("Append").
save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
records in Hudi table")
{code}
h2. *Analysis*
h3. *Upsert (400 entries)*
{code:java}
WARN HoodieMergeHandle:
Number of entries in MemoryBasedMap => 150875
Total size in bytes of MemoryBasedMap => 83886580
Number of entries in DiskBasedMap => 3849125
Size of file spilled to disk => 1443046132
{code}
h3. Hang stackstrace (DiskBasedMap#get)
{code:java}
"pool-21-thread-2" Id=696 cpuUsage=98% RUNNABLE
at java.util.zip.ZipFile.getEntry(Native Method)
at java.util.zip.ZipFile.getEntry(ZipFile.java:310)
- locked java.util.jar.JarFile@1fc27ed4
at java.util.jar.JarFile.getEntry(JarFile.java:240)
at java.util.jar.JarFile.getJarEntry(JarFile.java:223)
at sun.misc.URLClassPath$JarLoader.getResource(URLClassPath.java:1005)
at sun.misc.URLClassPath.getResource(URLClassPath.java:212)
at java.net.URLClassLoader$1.run(URLClassLoader.java:365)
at java.net.URLClassLoader$1.run(URLClassLoader.java:362)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:361)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
- locked java.lang.Object@28f65251
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331)
at java.lang.ClassLoader.loadClass(ClassLoader.java:411)
- locked
scala.reflect.internal.util.ScalaClassLoader$URLClassLoader@a353dff
at java.lang.ClassLoader.loadClass(ClassLoader.java:411)
- locked
[jira] [Updated] (HUDI-625) Address performance concerns on DiskBasedMap.get() during upsert of thin records
[
https://issues.apache.org/jira/browse/HUDI-625?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
lamber-ken updated HUDI-625:
Description:
[https://github.com/apache/incubator-hudi/issues/1328]
So what's going on here is that each entry (single data field) is estimated to
be around 500-750 bytes in memory and things spill a lot...
{code:java}
20/02/20 23:00:39 INFO ExternalSpillableMap: Estimated Payload size => 760 for
3675605,HoodieRecord{key=HoodieKey { recordKey=3675605 partitionPath=default},
currentLocation='HoodieRecordLocation {instantTime=20200220225748,
fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}',
newLocation='HoodieRecordLocation {instantTime=20200220225921,
fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}'} {code}
h2. Reproduce steps
{code:java}
export SPARK_HOME=/home/dockeradmin/hudi/spark-2.4.4-bin-hadoop2.7
${SPARK_HOME}/bin/spark-shell \
--executor-memory 6G \
--packages
org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4
\
--conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
{code}
{code:java}
val HUDI_FORMAT = "org.apache.hudi"
val TABLE_NAME = "hoodie.table.name"
val RECORDKEY_FIELD_OPT_KEY = "hoodie.datasource.write.recordkey.field"
val PRECOMBINE_FIELD_OPT_KEY = "hoodie.datasource.write.precombine.field"
val OPERATION_OPT_KEY = "hoodie.datasource.write.operation"
val BULK_INSERT_OPERATION_OPT_VAL = "bulk_insert"
val UPSERT_OPERATION_OPT_VAL = "upsert"
val BULK_INSERT_PARALLELISM = "hoodie.bulkinsert.shuffle.parallelism"
val UPSERT_PARALLELISM = "hoodie.upsert.shuffle.parallelism"
val config = Map(
"table_name" -> "example_table",
"target" -> "file:///tmp/example_table/",
"primary_key" -> "id",
"sort_key" -> "id"
)
val readPath = config("target") + "/*"val json_data = (1 to 400).map(i =>
"{\"id\":" + i + "}")
val jsonRDD = spark.sparkContext.parallelize(json_data, 2)
val df1 = spark.read.json(jsonRDD)
println(s"${df1.count()} records in source 1")
df1.write.format(HUDI_FORMAT).
option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
option(TABLE_NAME, config("table_name")).
option(OPERATION_OPT_KEY, BULK_INSERT_OPERATION_OPT_VAL).
option(BULK_INSERT_PARALLELISM, 1).
mode("Overwrite").
save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
records in Hudi table")
// Runs very slow
df1.limit(300).write.format(HUDI_FORMAT).
option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
option(TABLE_NAME, config("table_name")).
option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
option(UPSERT_PARALLELISM, 20).
mode("Append").
save(config("target"))
// Runs very slow
df1.write.format(HUDI_FORMAT).
option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
option(TABLE_NAME, config("table_name")).
option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
option(UPSERT_PARALLELISM, 20).
mode("Append").
save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
records in Hudi table")
{code}
h2. *Analysis*
h3. *Upsert (400 entries)*
{code:java}
WARN HoodieMergeHandle:
Number of entries in MemoryBasedMap => 150875
Total size in bytes of MemoryBasedMap => 83886580
Number of entries in DiskBasedMap => 3849125
Size of file spilled to disk => 1443046132
{code}
h3. Hang stackstrace (DiskBasedMap#get)
{code:java}
"pool-21-thread-2" Id=696 cpuUsage=98% RUNNABLE
at java.util.zip.ZipFile.getEntry(Native Method)
at java.util.zip.ZipFile.getEntry(ZipFile.java:310)
- locked java.util.jar.JarFile@1fc27ed4
at java.util.jar.JarFile.getEntry(JarFile.java:240)
at java.util.jar.JarFile.getJarEntry(JarFile.java:223)
at sun.misc.URLClassPath$JarLoader.getResource(URLClassPath.java:1005)
at sun.misc.URLClassPath.getResource(URLClassPath.java:212)
at java.net.URLClassLoader$1.run(URLClassLoader.java:365)
at java.net.URLClassLoader$1.run(URLClassLoader.java:362)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:361)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
- locked java.lang.Object@28f65251
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331)
at java.lang.ClassLoader.loadClass(ClassLoader.java:411)
- locked
scala.reflect.internal.util.ScalaClassLoader$URLClassLoader@a353dff
at java.lang.ClassLoader.loadClass(ClassLoader.java:411)
- locked com.esotericsoftware.reflectasm.AccessClassLoader@2c7122e2
at
com.esotericsoftware.reflectasm.AccessClassLoader.loadClass(AccessClassLoader.java:92)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at
[jira] [Updated] (HUDI-625) Address performance concerns on DiskBasedMap.get() during upsert of thin records
[
https://issues.apache.org/jira/browse/HUDI-625?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
lamber-ken updated HUDI-625:
Attachment: image-2020-02-21-15-35-56-637.png
> Address performance concerns on DiskBasedMap.get() during upsert of thin
> records
>
>
> Key: HUDI-625
> URL: https://issues.apache.org/jira/browse/HUDI-625
> Project: Apache Hudi (incubating)
> Issue Type: Improvement
> Components: Performance, Writer Core
>Reporter: Vinoth Chandar
>Assignee: Vinoth Chandar
>Priority: Major
> Fix For: 0.6.0
>
> Attachments: image-2020-02-20-23-34-24-155.png,
> image-2020-02-20-23-34-27-466.png, image-2020-02-21-15-35-56-637.png
>
>
> [https://github.com/apache/incubator-hudi/issues/1328]
>
> So what's going on here is that each entry (single data field) is estimated
> to be around 500-750 bytes in memory and things spill a lot...
> {code:java}
> 20/02/20 23:00:39 INFO ExternalSpillableMap: Estimated Payload size => 760
> for 3675605,HoodieRecord{key=HoodieKey { recordKey=3675605
> partitionPath=default}, currentLocation='HoodieRecordLocation
> {instantTime=20200220225748, fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}',
> newLocation='HoodieRecordLocation {instantTime=20200220225921,
> fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}'} {code}
>
> This is not too far from reality
> !image-2020-02-20-23-34-27-466.png|width=952,height=58!
> !image-2020-02-20-23-34-24-155.png|width=975,height=19!
>
>
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[jira] [Updated] (HUDI-625) Address performance concerns on DiskBasedMap.get() during upsert of thin records
[
https://issues.apache.org/jira/browse/HUDI-625?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
lamber-ken updated HUDI-625:
Description:
[https://github.com/apache/incubator-hudi/issues/1328]
So what's going on here is that each entry (single data field) is estimated to
be around 500-750 bytes in memory and things spill a lot...
{code:java}
20/02/20 23:00:39 INFO ExternalSpillableMap: Estimated Payload size => 760 for
3675605,HoodieRecord{key=HoodieKey { recordKey=3675605 partitionPath=default},
currentLocation='HoodieRecordLocation {instantTime=20200220225748,
fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}',
newLocation='HoodieRecordLocation {instantTime=20200220225921,
fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}'} {code}
h2. Reproduce steps
{code:java}
export SPARK_HOME=/home/dockeradmin/hudi/spark-2.4.4-bin-hadoop2.7
${SPARK_HOME}/bin/spark-shell \
--executor-memory 6G \
--packages
org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4
\
--conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
{code}
{code:java}
val HUDI_FORMAT = "org.apache.hudi"
val TABLE_NAME = "hoodie.table.name"
val RECORDKEY_FIELD_OPT_KEY = "hoodie.datasource.write.recordkey.field"
val PRECOMBINE_FIELD_OPT_KEY = "hoodie.datasource.write.precombine.field"
val OPERATION_OPT_KEY = "hoodie.datasource.write.operation"
val BULK_INSERT_OPERATION_OPT_VAL = "bulk_insert"
val UPSERT_OPERATION_OPT_VAL = "upsert"
val BULK_INSERT_PARALLELISM = "hoodie.bulkinsert.shuffle.parallelism"
val UPSERT_PARALLELISM = "hoodie.upsert.shuffle.parallelism"
val config = Map(
"table_name" -> "example_table",
"target" -> "file:///tmp/example_table/",
"primary_key" -> "id",
"sort_key" -> "id"
)
val readPath = config("target") + "/*"val json_data = (1 to 400).map(i =>
"{\"id\":" + i + "}")
val jsonRDD = spark.sparkContext.parallelize(json_data, 2)
val df1 = spark.read.json(jsonRDD)
println(s"${df1.count()} records in source 1")
df1.write.format(HUDI_FORMAT).
option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
option(TABLE_NAME, config("table_name")).
option(OPERATION_OPT_KEY, BULK_INSERT_OPERATION_OPT_VAL).
option(BULK_INSERT_PARALLELISM, 1).
mode("Overwrite").
save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
records in Hudi table")
// Runs very slow
df1.limit(300).write.format(HUDI_FORMAT).
option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
option(TABLE_NAME, config("table_name")).
option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
option(UPSERT_PARALLELISM, 20).
mode("Append").
save(config("target"))
// Runs very slow
df1.write.format(HUDI_FORMAT).
option(PRECOMBINE_FIELD_OPT_KEY, config("sort_key")).
option(RECORDKEY_FIELD_OPT_KEY, config("primary_key")).
option(TABLE_NAME, config("table_name")).
option(OPERATION_OPT_KEY, UPSERT_OPERATION_OPT_VAL).
option(UPSERT_PARALLELISM, 20).
mode("Append").
save(config("target"))println(s"${spark.read.format(HUDI_FORMAT).load(readPath).count()}
records in Hudi table")
{code}
h2. *Analysis*
h3. *Upsert (400 entries)*
{code:java}
WARN HoodieMergeHandle:
Number of entries in MemoryBasedMap => 150875
Total size in bytes of MemoryBasedMap => 83886580
Number of entries in DiskBasedMap => 3849125
Size of file spilled to disk => 1443046132
{code}
h3. Hang stackstrace (DiskBasedMap#get)
{code:java}
"pool-21-thread-2" Id=696 cpuUsage=98% RUNNABLE
at java.util.zip.ZipFile.getEntry(Native Method)
at java.util.zip.ZipFile.getEntry(ZipFile.java:310)
- locked java.util.jar.JarFile@1fc27ed4
at java.util.jar.JarFile.getEntry(JarFile.java:240)
at java.util.jar.JarFile.getJarEntry(JarFile.java:223)
at sun.misc.URLClassPath$JarLoader.getResource(URLClassPath.java:1005)
at sun.misc.URLClassPath.getResource(URLClassPath.java:212)
at java.net.URLClassLoader$1.run(URLClassLoader.java:365)
at java.net.URLClassLoader$1.run(URLClassLoader.java:362)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:361)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
- locked java.lang.Object@28f65251
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331)
at java.lang.ClassLoader.loadClass(ClassLoader.java:411)
- locked
scala.reflect.internal.util.ScalaClassLoader$URLClassLoader@a353dff
at java.lang.ClassLoader.loadClass(ClassLoader.java:411)
- locked com.esotericsoftware.reflectasm.AccessClassLoader@2c7122e2
at
com.esotericsoftware.reflectasm.AccessClassLoader.loadClass(AccessClassLoader.java:92)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at
[jira] [Updated] (HUDI-625) Address performance concerns on DiskBasedMap.get() during upsert of thin records
[
https://issues.apache.org/jira/browse/HUDI-625?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Vinoth Chandar updated HUDI-625:
Summary: Address performance concerns on DiskBasedMap.get() during upsert
of thin records (was: Address performance concerns on DiskBasedMap.get()
during upsert of small workload )
> Address performance concerns on DiskBasedMap.get() during upsert of thin
> records
>
>
> Key: HUDI-625
> URL: https://issues.apache.org/jira/browse/HUDI-625
> Project: Apache Hudi (incubating)
> Issue Type: Improvement
> Components: Performance, Writer Core
>Reporter: Vinoth Chandar
>Assignee: Vinoth Chandar
>Priority: Major
> Fix For: 0.6.0
>
> Attachments: image-2020-02-20-23-34-24-155.png,
> image-2020-02-20-23-34-27-466.png
>
>
> [https://github.com/apache/incubator-hudi/issues/1328]
>
> So what's going on here is that each entry (single data field) is estimated
> to be around 500-750 bytes in memory and things spill a lot...
> {code:java}
> 20/02/20 23:00:39 INFO ExternalSpillableMap: Estimated Payload size => 760
> for 3675605,HoodieRecord{key=HoodieKey { recordKey=3675605
> partitionPath=default}, currentLocation='HoodieRecordLocation
> {instantTime=20200220225748, fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}',
> newLocation='HoodieRecordLocation {instantTime=20200220225921,
> fileId=499f8d2c-df6a-4275-9166-3de4ac91f3bf-0}'} {code}
>
> This is not too far from reality
> !image-2020-02-20-23-34-27-466.png|width=952,height=58!
> !image-2020-02-20-23-34-24-155.png|width=975,height=19!
>
>
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