a new messaging-based log aggregator
a distributed messaging system
horizontally scalable messaging system.
Memory Mapped Files
Kernel Space processing
Kylin1027
/ 0: jvm-options-java8.conf
Created
April 10, 2018 13:29
— forked from elifarley/0: jvm-options-java8.conf
JVM options to maximize performance
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| # See https://docs.oracle.com/javase/8/docs/technotes/tools/windows/java.html | |
| # See https://docs.oracle.com/javase/8/docs/technotes/guides/vm/performance-enhancements-7.html | |
| # See https://docs.oracle.com/javase/8/embedded/develop-apps-platforms/codecache.htm | |
| # See http://normanmaurer.me/blog_in_progress/2013/11/07/Inline-all-the-Things/ | |
| # See http://stas-blogspot.blogspot.com.br/2011/07/most-complete-list-of-xx-options-for.html | |
| # -XX:+LogCompilation | |
| # -XX:+PrintInlining | |
| -Dfile.encoding=UTF-8 |
Kylin1027
/ kafka_notes.md
Created
December 14, 2017 04:13
— forked from topher6345/kafka_notes.md
Kylin1027
/ metrics.properties
Created
December 6, 2017 07:25
— forked from ryan-williams/metrics.properties
Spark metrics.properties example
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| # Enable Graphite | |
| *.sink.graphite.class=org.apache.spark.metrics.sink.GraphiteSink | |
| *.sink.graphite.host=<graphite host> | |
| *.sink.graphite.port=<graphite port> | |
| *.sink.graphite.period=10 | |
| # Enable jvm source for instance master, worker, driver and executor | |
| master.source.jvm.class=org.apache.spark.metrics.source.JvmSource | |
| worker.source.jvm.class=org.apache.spark.metrics.source.JvmSource | |
| driver.source.jvm.class=org.apache.spark.metrics.source.JvmSource |
Kylin1027
/ example.scala
Created
May 19, 2017 11:13
— forked from andrearota/example.scala
Creating Spark UDF with extra parameters via currying
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| // Problem: creating a Spark UDF that take extra parameter at invocation time. | |
| // Solution: using currying | |
| // http://stackoverflow.com/questions/35546576/how-can-i-pass-extra-parameters-to-udfs-in-sparksql | |
| // We want to create hideTabooValues, a Spark UDF that set to -1 fields that contains any of given taboo values. | |
| // E.g. forbiddenValues = [1, 2, 3] | |
| // dataframe = [1, 2, 3, 4, 5, 6] | |
| // dataframe.select(hideTabooValues(forbiddenValues)) :> [-1, -1, -1, 4, 5, 6] | |
| // | |
| // Implementing this in Spark, we find two major issues: |