[jira] [Updated] (SPARK-22784) Configure reading buffer size in Spark History Server
[ https://issues.apache.org/jira/browse/SPARK-22784?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mikhail Erofeev updated SPARK-22784: Description: Motivation: Our Spark History Server spends most of the backfill time inside BufferedReader and StringBuffer. It happens because average line size of our events is ~1.500.000 chars (due to a lot of partitions and iterations), whereas the default buffer size is 2048 bytes. See the attached flame graph. Implementation: I've added logging of spent time and line size for each job. Parametrised ReplayListenerBus with a new buffer size parameter. Measured the best buffer size. x20 of the average line size (30mb) gives 32% speedup in a local test. Result: Backfill of Spark History and reading to the cache will be up to 30% faster after tuning. was: Motivation: Our Spark History Server spends most of its warm-up time inside BufferedReader and StringBuffer. It happens because average line size of our events is ~1.500.000 chars (due to a lot of partitions and iterations), whereas the default buffer size is 2048 bytes. See the attached flame graph. Implementation: I've added logging of spent time and line size for each job. Parametrised ReplayListenerBus with new buffer size parameter. Measured best buffer size. x20 of average line size (30mb) gives 32% speedup in a local test. Result: Warm-up of Spark History and reading to cache will be up to 30% faster after tuning. > Configure reading buffer size in Spark History Server > - > > Key: SPARK-22784 > URL: https://issues.apache.org/jira/browse/SPARK-22784 > Project: Spark > Issue Type: Improvement > Components: Spark Core >Affects Versions: 2.2.1 >Reporter: Mikhail Erofeev >Priority: Minor > Attachments: replay-baseline.svg > > > Motivation: > Our Spark History Server spends most of the backfill time inside > BufferedReader and StringBuffer. It happens because average line size of our > events is ~1.500.000 chars (due to a lot of partitions and iterations), > whereas the default buffer size is 2048 bytes. See the attached flame graph. > Implementation: > I've added logging of spent time and line size for each job. > Parametrised ReplayListenerBus with a new buffer size parameter. > Measured the best buffer size. x20 of the average line size (30mb) gives 32% > speedup in a local test. > Result: > Backfill of Spark History and reading to the cache will be up to 30% faster > after tuning. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22784) Configure reading buffer size in Spark History Server
[ https://issues.apache.org/jira/browse/SPARK-22784?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mikhail Erofeev updated SPARK-22784: Attachment: replay-baseline.svg > Configure reading buffer size in Spark History Server > - > > Key: SPARK-22784 > URL: https://issues.apache.org/jira/browse/SPARK-22784 > Project: Spark > Issue Type: Improvement > Components: Spark Core >Affects Versions: 2.2.1 >Reporter: Mikhail Erofeev >Priority: Minor > Attachments: replay-baseline.svg > > > Motivation: > Our Spark History Server spends most of its warm-up time inside > BufferedReader and StringBuffer. It happens because average line size of our > events is ~1.500.000 chars (due to a lot of partitions and iterations), > whereas the default buffer size is 2048 bytes. See the attached flame graph. > Implementation: > I've added logging of spent time and line size for each job. > Parametrised ReplayListenerBus with new buffer size parameter. > Measured best buffer size. x20 of average line size (30mb) gives 32% speedup > in a local test. > Result: > Warm-up of Spark History and reading to cache will be up to 30% faster after > tuning. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22784) Increase reading buffer size in Spark History Server
[ https://issues.apache.org/jira/browse/SPARK-22784?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mikhail Erofeev updated SPARK-22784: Affects Version/s: (was: 2.0.0) 2.2.1 Target Version/s: (was: 2.2.1) > Increase reading buffer size in Spark History Server > > > Key: SPARK-22784 > URL: https://issues.apache.org/jira/browse/SPARK-22784 > Project: Spark > Issue Type: Improvement > Components: Spark Core >Affects Versions: 2.2.1 >Reporter: Mikhail Erofeev >Priority: Minor > > Motivation: > Our Spark History Server spends most of its warm-up time inside > BufferedReader and StringBuffer. It happens because average line size of our > events is ~1.500.000 chars (due to a lot of partitions and iterations), > whereas the default buffer size is 2048 bytes. See the attached flame graph. > Implementation: > I've added logging of spent time and line size for each job. > Parametrised ReplayListenerBus with new buffer size parameter. > Measured best buffer size. x20 of average line size (30mb) gives 32% speedup > in a local test. > Result: > Warm-up of Spark History and reading to cache will be up to 30% faster after > tuning. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22784) Configure reading buffer size in Spark History Server
[ https://issues.apache.org/jira/browse/SPARK-22784?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mikhail Erofeev updated SPARK-22784: Summary: Configure reading buffer size in Spark History Server (was: Increase reading buffer size in Spark History Server) > Configure reading buffer size in Spark History Server > - > > Key: SPARK-22784 > URL: https://issues.apache.org/jira/browse/SPARK-22784 > Project: Spark > Issue Type: Improvement > Components: Spark Core >Affects Versions: 2.2.1 >Reporter: Mikhail Erofeev >Priority: Minor > > Motivation: > Our Spark History Server spends most of its warm-up time inside > BufferedReader and StringBuffer. It happens because average line size of our > events is ~1.500.000 chars (due to a lot of partitions and iterations), > whereas the default buffer size is 2048 bytes. See the attached flame graph. > Implementation: > I've added logging of spent time and line size for each job. > Parametrised ReplayListenerBus with new buffer size parameter. > Measured best buffer size. x20 of average line size (30mb) gives 32% speedup > in a local test. > Result: > Warm-up of Spark History and reading to cache will be up to 30% faster after > tuning. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Created] (SPARK-22784) Increase reading buffer size in Spark History Server
Mikhail Erofeev created SPARK-22784: --- Summary: Increase reading buffer size in Spark History Server Key: SPARK-22784 URL: https://issues.apache.org/jira/browse/SPARK-22784 Project: Spark Issue Type: Improvement Components: Spark Core Affects Versions: 2.0.0 Reporter: Mikhail Erofeev Priority: Minor Motivation: Our Spark History Server spends most of its warm-up time inside BufferedReader and StringBuffer. It happens because average line size of our events is ~1.500.000 chars (due to a lot of partitions and iterations), whereas the default buffer size is 2048 bytes. See the attached flame graph. Implementation: I've added logging of spent time and line size for each job. Parametrised ReplayListenerBus with new buffer size parameter. Measured best buffer size. x20 of average line size (30mb) gives 32% speedup in a local test. Result: Warm-up of Spark History and reading to cache will be up to 30% faster after tuning. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
