spark-instrumented-optimizer/dev/deps/spark-deps-hadoop-2.7

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[SPARK-14280][BUILD][WIP] Update change-version.sh and pom.xml to add Scala 2.12 profiles and enable 2.12 compilation …build; fix some things that will be warnings or errors in 2.12; restore Scala 2.12 profile infrastructure ## What changes were proposed in this pull request? This change adds back the infrastructure for a Scala 2.12 build, but does not enable it in the release or Python test scripts. In order to make that meaningful, it also resolves compile errors that the code hits in 2.12 only, in a way that still works with 2.11. It also updates dependencies to the earliest minor release of dependencies whose current version does not yet support Scala 2.12. This is in a sense covered by other JIRAs under the main umbrella, but implemented here. The versions below still work with 2.11, and are the _latest_ maintenance release in the _earliest_ viable minor release. - Scalatest 2.x -> 3.0.3 - Chill 0.8.0 -> 0.8.4 - Clapper 1.0.x -> 1.1.2 - json4s 3.2.x -> 3.4.2 - Jackson 2.6.x -> 2.7.9 (required by json4s) This change does _not_ fully enable a Scala 2.12 build: - It will also require dropping support for Kafka before 0.10. Easy enough, just didn't do it yet here - It will require recreating `SparkILoop` and `Main` for REPL 2.12, which is SPARK-14650. Possible to do here too. What it does do is make changes that resolve much of the remaining gap without affecting the current 2.11 build. ## How was this patch tested? Existing tests and build. Manually tested with `./dev/change-scala-version.sh 2.12` to verify it compiles, modulo the exceptions above. Author: Sean Owen <sowen@cloudera.com> Closes #18645 from srowen/SPARK-14280.
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chill-java-0.8.4.jar
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commons-beanutils-1.7.0.jar
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[SPARK-13534][PYSPARK] Using Apache Arrow to increase performance of DataFrame.toPandas ## What changes were proposed in this pull request? Integrate Apache Arrow with Spark to increase performance of `DataFrame.toPandas`. This has been done by using Arrow to convert data partitions on the executor JVM to Arrow payload byte arrays where they are then served to the Python process. The Python DataFrame can then collect the Arrow payloads where they are combined and converted to a Pandas DataFrame. Data types except complex, date, timestamp, and decimal are currently supported, otherwise an `UnsupportedOperation` exception is thrown. Additions to Spark include a Scala package private method `Dataset.toArrowPayload` that will convert data partitions in the executor JVM to `ArrowPayload`s as byte arrays so they can be easily served. A package private class/object `ArrowConverters` that provide data type mappings and conversion routines. In Python, a private method `DataFrame._collectAsArrow` is added to collect Arrow payloads and a SQLConf "spark.sql.execution.arrow.enable" can be used in `toPandas()` to enable using Arrow (uses the old conversion by default). ## How was this patch tested? Added a new test suite `ArrowConvertersSuite` that will run tests on conversion of Datasets to Arrow payloads for supported types. The suite will generate a Dataset and matching Arrow JSON data, then the dataset is converted to an Arrow payload and finally validated against the JSON data. This will ensure that the schema and data has been converted correctly. Added PySpark tests to verify the `toPandas` method is producing equal DataFrames with and without pyarrow. A roundtrip test to ensure the pandas DataFrame produced by pyspark is equal to a one made directly with pandas. Author: Bryan Cutler <cutlerb@gmail.com> Author: Li Jin <ice.xelloss@gmail.com> Author: Li Jin <li.jin@twosigma.com> Author: Wes McKinney <wes.mckinney@twosigma.com> Closes #18459 from BryanCutler/toPandas_with_arrow-SPARK-13534.
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flatbuffers-1.2.0-3f79e055.jar
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[SPARK-24418][BUILD] Upgrade Scala to 2.11.12 and 2.12.6 ## What changes were proposed in this pull request? Scala is upgraded to `2.11.12` and `2.12.6`. We used `loadFIles()` in `ILoop` as a hook to initialize the Spark before REPL sees any files in Scala `2.11.8`. However, it was a hack, and it was not intended to be a public API, so it was removed in Scala `2.11.12`. From the discussion in Scala community, https://github.com/scala/bug/issues/10913 , we can use `initializeSynchronous` to initialize Spark instead. This PR implements the Spark initialization there. However, in Scala `2.11.12`'s `ILoop.scala`, in function `def startup()`, the first thing it calls is `printWelcome()`. As a result, Scala will call `printWelcome()` and `splash` before calling `initializeSynchronous`. Thus, the Spark shell will allow users to type commends first, and then show the Spark UI URL. It's working, but it will change the Spark Shell interface as the following. ```scala ➜ apache-spark git:(scala-2.11.12) ✗ ./bin/spark-shell Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). Welcome to ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/ /___/ .__/\_,_/_/ /_/\_\ version 2.4.0-SNAPSHOT /_/ Using Scala version 2.11.12 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_161) Type in expressions to have them evaluated. Type :help for more information. scala> Spark context Web UI available at http://192.168.1.169:4040 Spark context available as 'sc' (master = local[*], app id = local-1528180279528). Spark session available as 'spark'. scala> ``` It seems there is no easy way to inject the Spark initialization code in the proper place as Scala doesn't provide a hook. Maybe som-snytt can comment on this. The following command is used to update the dep files. ```scala ./dev/test-dependencies.sh --replace-manifest ``` ## How was this patch tested? Existing tests Author: DB Tsai <d_tsai@apple.com> Closes #21495 from dbtsai/scala-2.11.12.
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[SPARK-18935][MESOS] Fix dynamic reservations on mesos ## What changes were proposed in this pull request? - Solves the issue described in the ticket by preserving reservation and allocation info in all cases (port handling included). - upgrades to 1.4 - Adds extra debug level logging to make debugging easier in the future, for example we add reservation info when applicable. ``` 17/09/29 14:53:07 DEBUG MesosCoarseGrainedSchedulerBackend: Accepting offer: f20de49b-dee3-45dd-a3c1-73418b7de891-O32 with attributes: Map() allocation info: role: "spark-prive" reservation info: name: "ports" type: RANGES ranges { range { begin: 31000 end: 32000 } } role: "spark-prive" reservation { principal: "test" } allocation_info { role: "spark-prive" } ``` - Some style cleanup. ## How was this patch tested? Manually by running the example in the ticket with and without a principal. Specifically I tested it on a dc/os 1.10 cluster with 7 nodes and played with reservations. From the master node in order to reserve resources I executed: ```for i in 0 1 2 3 4 5 6 do curl -i \ -d slaveId=90ec65ea-1f7b-479f-a824-35d2527d6d26-S$i \ -d resources='[ { "name": "cpus", "type": "SCALAR", "scalar": { "value": 2 }, "role": "spark-role", "reservation": { "principal": "" } }, { "name": "mem", "type": "SCALAR", "scalar": { "value": 8026 }, "role": "spark-role", "reservation": { "principal": "" } } ]' \ -X POST http://master.mesos:5050/master/reserve done ``` Nodes had 4 cpus (m3.xlarge instances) and I reserved either 2 or 4 cpus (all for a role). I verified it launches tasks on nodes with reserved resources under `spark-role` role only if a) there are remaining resources for (*) default role and the spark driver has no role assigned to it. b) the spark driver has a role assigned to it and it is the same role used in reservations. I also tested this locally on my machine. Author: Stavros Kontopoulos <st.kontopoulos@gmail.com> Closes #19390 from skonto/fix_dynamic_reservation.
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[SPARK-24418][BUILD] Upgrade Scala to 2.11.12 and 2.12.6 ## What changes were proposed in this pull request? Scala is upgraded to `2.11.12` and `2.12.6`. We used `loadFIles()` in `ILoop` as a hook to initialize the Spark before REPL sees any files in Scala `2.11.8`. However, it was a hack, and it was not intended to be a public API, so it was removed in Scala `2.11.12`. From the discussion in Scala community, https://github.com/scala/bug/issues/10913 , we can use `initializeSynchronous` to initialize Spark instead. This PR implements the Spark initialization there. However, in Scala `2.11.12`'s `ILoop.scala`, in function `def startup()`, the first thing it calls is `printWelcome()`. As a result, Scala will call `printWelcome()` and `splash` before calling `initializeSynchronous`. Thus, the Spark shell will allow users to type commends first, and then show the Spark UI URL. It's working, but it will change the Spark Shell interface as the following. ```scala ➜ apache-spark git:(scala-2.11.12) ✗ ./bin/spark-shell Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). Welcome to ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/ /___/ .__/\_,_/_/ /_/\_\ version 2.4.0-SNAPSHOT /_/ Using Scala version 2.11.12 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_161) Type in expressions to have them evaluated. Type :help for more information. scala> Spark context Web UI available at http://192.168.1.169:4040 Spark context available as 'sc' (master = local[*], app id = local-1528180279528). Spark session available as 'spark'. scala> ``` It seems there is no easy way to inject the Spark initialization code in the proper place as Scala doesn't provide a hook. Maybe som-snytt can comment on this. The following command is used to update the dep files. ```scala ./dev/test-dependencies.sh --replace-manifest ``` ## How was this patch tested? Existing tests Author: DB Tsai <d_tsai@apple.com> Closes #21495 from dbtsai/scala-2.11.12.
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scala-compiler-2.11.12.jar
scala-library-2.11.12.jar
scala-parser-combinators_2.11-1.1.0.jar
scala-reflect-2.11.12.jar
[SPARK-14280][BUILD][WIP] Update change-version.sh and pom.xml to add Scala 2.12 profiles and enable 2.12 compilation …build; fix some things that will be warnings or errors in 2.12; restore Scala 2.12 profile infrastructure ## What changes were proposed in this pull request? This change adds back the infrastructure for a Scala 2.12 build, but does not enable it in the release or Python test scripts. In order to make that meaningful, it also resolves compile errors that the code hits in 2.12 only, in a way that still works with 2.11. It also updates dependencies to the earliest minor release of dependencies whose current version does not yet support Scala 2.12. This is in a sense covered by other JIRAs under the main umbrella, but implemented here. The versions below still work with 2.11, and are the _latest_ maintenance release in the _earliest_ viable minor release. - Scalatest 2.x -> 3.0.3 - Chill 0.8.0 -> 0.8.4 - Clapper 1.0.x -> 1.1.2 - json4s 3.2.x -> 3.4.2 - Jackson 2.6.x -> 2.7.9 (required by json4s) This change does _not_ fully enable a Scala 2.12 build: - It will also require dropping support for Kafka before 0.10. Easy enough, just didn't do it yet here - It will require recreating `SparkILoop` and `Main` for REPL 2.12, which is SPARK-14650. Possible to do here too. What it does do is make changes that resolve much of the remaining gap without affecting the current 2.11 build. ## How was this patch tested? Existing tests and build. Manually tested with `./dev/change-scala-version.sh 2.12` to verify it compiles, modulo the exceptions above. Author: Sean Owen <sowen@cloudera.com> Closes #18645 from srowen/SPARK-14280.
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scala-xml_2.11-1.0.5.jar
shapeless_2.11-2.3.2.jar
slf4j-api-1.7.16.jar
slf4j-log4j12-1.7.16.jar
snakeyaml-1.15.jar
snappy-0.2.jar
snappy-java-1.1.7.1.jar
spire-macros_2.11-0.13.0.jar
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stax-api-1.0-2.jar
stax-api-1.0.1.jar
stream-2.7.0.jar
stringtemplate-3.2.1.jar
super-csv-2.2.0.jar
[SPARK-24945][SQL] Switching to uniVocity 2.7.3 ## What changes were proposed in this pull request? In the PR, I propose to upgrade uniVocity parser from **2.6.3** to **2.7.3**. The recent version includes a fix for the SPARK-24645 issue and has better performance. Before changes: ``` Parsing quoted values: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ One quoted string 33336 / 34122 0.0 666727.0 1.0X Wide rows with 1000 columns: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Select 1000 columns 90287 / 91713 0.0 90286.9 1.0X Select 100 columns 31826 / 36589 0.0 31826.4 2.8X Select one column 25738 / 25872 0.0 25737.9 3.5X count() 6931 / 7269 0.1 6931.5 13.0X ``` after: ``` Parsing quoted values: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ One quoted string 33411 / 33510 0.0 668211.4 1.0X Wide rows with 1000 columns: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Select 1000 columns 88028 / 89311 0.0 88028.1 1.0X Select 100 columns 29010 / 32755 0.0 29010.1 3.0X Select one column 22936 / 22953 0.0 22936.5 3.8X count() 6657 / 6740 0.2 6656.6 13.5X ``` Closes #21892 ## How was this patch tested? It was tested by `CSVSuite` and `CSVBenchmarks` Author: Maxim Gekk <maxim.gekk@databricks.com> Closes #21969 from MaxGekk/univocity-2_7_3.
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univocity-parsers-2.7.3.jar
validation-api-1.1.0.Final.jar
xbean-asm6-shaded-4.8.jar
xercesImpl-2.9.1.jar
xmlenc-0.52.jar
xz-1.5.jar
zjsonpatch-0.3.0.jar
zookeeper-3.4.6.jar
zstd-jni-1.3.2-2.jar