SizeBasedRollingPolicy print too many log when spark.executor.logs.rolling.strategy is size , shouldRollover use logInfo method:
def shouldRollover(bytesToBeWritten: Long): Boolean = {
logInfo(s"$bytesToBeWritten + $bytesWrittenSinceRollover > $rolloverSizeBytes")
bytesToBeWritten + bytesWrittenSinceRollover > rolloverSizeBytes
}
logs.rolling:
spark.executor.logs.rolling.strategy size
spark.executor.logs.rolling.maxSize 134217728
spark.executor.logs.rolling.maxRetainedFiles 8
Can use logdebug instead of loginfo ?
------------------
Best Regards
Ricky Yang
------------------ 原始邮件 ------------------
发件人: "Jeff Zhang";<zj...@gmail.com>;
发送时间: 2015年12月20日(星期天) 下午3:44
收件人: "Luciano Resende"<lu...@gmail.com>;
抄送: "Michael Armbrust"<mi...@databricks.com>; "dev@spark.apache.org"<de...@spark.apache.org>;
主题: Re: [VOTE] Release Apache Spark 1.6.0 (RC3)
+1 (non-binding)
All the test passed, and run it on HDP 2.3.2 sandbox successfully.
On Sun, Dec 20, 2015 at 10:43 AM, Luciano Resende < luckbr1975@gmail.com > wrote:
+1 (non-binding)
Tested Standalone mode, SparkR and couple Stream Apps, all seem ok.
On Wed, Dec 16, 2015 at 1:32 PM, Michael Armbrust < michael@databricks.com > wrote:
Please vote on releasing the following candidate as Apache Spark version 1.6.0!
The vote is open until Saturday, December 19, 2015 at 18 :00 UTC and passes if a majority of at least 3 +1 PMC votes are cast.
[ ] +1 Release this package as Apache Spark 1.6.0
[ ] -1 Do not release this package because ...
To learn more about Apache Spark, please see http://spark.apache.org/
The tag to be voted on is v1.6.0-rc3 (168c89e07c51fa24b0bb88582c739cec0acb44d7)
The release files, including signatures, digests, etc. can be found at:
http://people.apache.org/~pwendell/spark-releases/spark-1.6.0-rc3-bin/
Release artifacts are signed with the following key:
https://people.apache.org/keys/committer/pwendell.asc
The staging repository for this release can be found at:
https://repository.apache.org/content/repositories/orgapachespark-1174/
The test repository (versioned as v1.6.0-rc3) for this release can be found at:
https://repository.apache.org/content/repositories/orgapachespark-1173/
The documentation corresponding to this release can be found at:
http://people.apache.org/~pwendell/spark-releases/spark-1.6.0-rc3-docs/
=======================================
== How can I help test this release? ==
=======================================
If you are a Spark user, you can help us test this release by taking an existing Spark workload and running on this release candidate, then reporting any regressions.
================================================
== What justifies a -1 vote for this release? ==
================================================
This vote is happening towards the end of the 1.6 QA period, so -1 votes should only occur for significant regressions from 1.5. Bugs already present in 1.5, minor regressions, or bugs related to new features will not block this release.
===============================================================
== What should happen to JIRA tickets still targeting 1.6.0? ==
===============================================================
1. It is OK for documentation patches to target 1.6.0 and still go into branch-1.6, since documentations will be published separately from the release.
2. New features for non-alpha-modules should target 1.7+.
3. Non-blocker bug fixes should target 1.6.1 or 1.7.0, or drop the target version.
==================================================
== Major changes to help you focus your testing ==
==================================================
Notable changes since 1.6 RC2
- SPARK_VERSION has been set correctly
- SPARK-12199 ML Docs are publishing correctly
- SPARK-12345 Mesos cluster mode has been fixed
Notable changes since 1.6 RC1
Spark Streaming
SPARK-2629 trackStateByKey has been renamed to mapWithState
Spark SQL
SPARK-12165 SPARK-12189 Fix bugs in eviction of storage memory by execution.
SPARK-12258 correct passing null into ScalaUDF
Notable Features Since 1.5
Spark SQL
SPARK-11787 Parquet Performance - Improve Parquet scan performance when using flat schemas.
SPARK-10810 Session Management - Isolated devault database (i.e USE mydb ) even on shared clusters.
SPARK-9999 Dataset API - A type-safe API (similar to RDDs) that performs many operations on serialized binary data and code generation (i.e. Project Tungsten).
SPARK-10000 Unified Memory Management - Shared memory for execution and caching instead of exclusive division of the regions.
SPARK-11197 SQL Queries on Files - Concise syntax for running SQL queries over files of any supported format without registering a table.
SPARK-11745 Reading non-standard JSON files - Added options to read non-standard JSON files (e.g. single-quotes, unquoted attributes)
SPARK-10412 Per-operator Metrics for SQL Execution - Display statistics on a peroperator basis for memory usage and spilled data size.
SPARK-11329 Star (*) expansion for StructTypes - Makes it easier to nest and unest arbitrary numbers of columns
SPARK-10917 , SPARK-11149 In-memory Columnar Cache Performance - Significant (up to 14x) speed up when caching data that contains complex types in DataFrames or SQL.
SPARK-11111 Fast null-safe joins - Joins using null-safe equality ( <=> ) will now execute using SortMergeJoin instead of computing a cartisian product.
SPARK-11389 SQL Execution Using Off-Heap Memory - Support for configuring query execution to occur using off-heap memory to avoid GC overhead
SPARK-10978 Datasource API Avoid Double Filter - When implemeting a datasource with filter pushdown, developers can now tell Spark SQL to avoid double evaluating a pushed-down filter.
SPARK-4849 Advanced Layout of Cached Data - storing partitioning and ordering schemes in In-memory table scan, and adding distributeBy and localSort to DF API
SPARK-9858 Adaptive query execution - Intial support for automatically selecting the number of reducers for joins and aggregations.
SPARK-9241 Improved query planner for queries having distinct aggregations - Query plans of distinct aggregations are more robust when distinct columns have high cardinality.
Spark Streaming
API Updates
SPARK-2629 New improved state management - mapWithState - a DStream transformation for stateful stream processing, supercedes updateStateByKey in functionality and performance.
SPARK-11198 Kinesis record deaggregation - Kinesis streams have been upgraded to use KCL 1.4.0 and supports transparent deaggregation of KPL-aggregated records.
SPARK-10891 Kinesis message handler function - Allows arbitraray function to be applied to a Kinesis record in the Kinesis receiver before to customize what data is to be stored in memory.
SPARK-6328 Python Streamng Listener API - Get streaming statistics (scheduling delays, batch processing times, etc.) in streaming.
UI Improvements
Made failures visible in the streaming tab, in the timelines, batch list, and batch details page.
Made output operations visible in the streaming tab as progress bars.
MLlib
New algorithms/models
SPARK-8518 Survival analysis - Log-linear model for survival analysis
SPARK-9834 Normal equation for least squares - Normal equation solver, providing R-like model summary statistics
SPARK-3147 Online hypothesis testing - A/B testing in the Spark Streaming framework
SPARK-9930 New feature transformers - ChiSqSelector, QuantileDiscretizer, SQL transformer
SPARK-6517 Bisecting K-Means clustering - Fast top-down clustering variant of K-Means
API improvements
ML Pipelines
SPARK-6725 Pipeline persistence - Save/load for ML Pipelines, with partial coverage of spark.ml algorithms
SPARK-5565 LDA in ML Pipelines - API for Latent Dirichlet Allocation in ML Pipelines
R API
SPARK-9836 R-like statistics for GLMs - (Partial) R-like stats for ordinary least squares via summary(model)
SPARK-9681 Feature interactions in R formula - Interaction operator ":" in R formula
Python API - Many improvements to Python API to approach feature parity
Misc improvements
SPARK-7685 , SPARK-9642 Instance weights for GLMs - Logistic and Linear Regression can take instance weights
SPARK-10384 , SPARK-10385 Univariate and bivariate statistics in DataFrames - Variance, stddev, correlations, etc.
SPARK-10117 LIBSVM data source - LIBSVM as a SQL data source
Documentation improvements
SPARK-7751 @since versions - Documentation includes initial version when classes and methods were added
SPARK-11337 Testable example code - Automated testing for code in user guide examples
Deprecations
In spark.mllib.clustering.KMeans, the "runs" parameter has been deprecated.
In spark.ml.classification.LogisticRegressionModel and spark.ml.regression.LinearRegressionModel, the "weights" field has been deprecated, in favor of the new name "coefficients." This helps disambiguate from instance (row) weights given to algorithms.
Changes of behavior
spark.mllib.tree.GradientBoostedTrees validationTol has changed semantics in 1.6. Previously, it was a threshold for absolute change in error. Now, it resembles the behavior of GradientDescent convergenceTol: For large errors, it uses relative error (relative to the previous error); for small errors (< 0.01), it uses absolute error.
spark.ml.feature.RegexTokenizer: Previously, it did not convert strings to lowercase before tokenizing. Now, it converts to lowercase by default, with an option not to. This matches the behavior of the simpler Tokenizer transformer.
Spark SQL's partition discovery has been changed to only discover partition directories that are children of the given path. (i.e. if path="/my/data/x=1" then x=1 will no longer be considered a partition but only children of x=1 .) This behavior can be overridden by manually specifying the basePath that partitioning discovery should start with ( SPARK-11678 ).
When casting a value of an integral type to timestamp (e.g. casting a long value to timestamp), the value is treated as being in seconds instead of milliseconds ( SPARK-11724 ).
With the improved query planner for queries having distinct aggregations ( SPARK-9241 ), the plan of a query having a single distinct aggregation has been changed to a more robust version. To switch back to the plan generated by Spark 1.5's planner, please set spark.sql.specializeSingleDistinctAggPlanning to true ( SPARK-12077 ).
--
Luciano Resende
http://people.apache.org/~lresende
http://twitter.com/lresende1975
http://lresende.blogspot.com/
--
Best Regards
Jeff Zhang