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

203 lines
4.9 KiB
Groff
Raw Normal View History

JavaEWAH-0.3.2.jar
RoaringBitmap-0.5.11.jar
ST4-4.0.4.jar
activation-1.1.1.jar
aircompressor-0.8.jar
antlr-2.7.7.jar
antlr-runtime-3.4.jar
2017-08-24 19:33:55 -04:00
antlr4-runtime-4.7.jar
aopalliance-1.0.jar
aopalliance-repackaged-2.4.0-b34.jar
apache-log4j-extras-1.2.17.jar
apacheds-i18n-2.0.0-M15.jar
apacheds-kerberos-codec-2.0.0-M15.jar
api-asn1-api-1.0.0-M20.jar
api-util-1.0.0-M20.jar
arpack_combined_all-0.1.jar
arrow-format-0.8.0.jar
arrow-memory-0.8.0.jar
arrow-vector-0.8.0.jar
automaton-1.11-8.jar
avro-1.7.7.jar
avro-ipc-1.7.7.jar
avro-mapred-1.7.7-hadoop2.jar
base64-2.3.8.jar
bcprov-jdk15on-1.58.jar
bonecp-0.8.0.RELEASE.jar
breeze-macros_2.11-0.13.2.jar
breeze_2.11-0.13.2.jar
calcite-avatica-1.2.0-incubating.jar
calcite-core-1.2.0-incubating.jar
calcite-linq4j-1.2.0-incubating.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.
2017-09-01 14:21:21 -04:00
chill-java-0.8.4.jar
chill_2.11-0.8.4.jar
commons-beanutils-1.7.0.jar
commons-beanutils-core-1.8.0.jar
commons-cli-1.2.jar
commons-codec-1.10.jar
commons-collections-3.2.2.jar
commons-compiler-3.0.8.jar
commons-compress-1.4.1.jar
commons-configuration-1.6.jar
commons-crypto-1.0.0.jar
commons-dbcp-1.4.jar
commons-digester-1.8.jar
commons-httpclient-3.1.jar
commons-io-2.4.jar
commons-lang-2.6.jar
commons-lang3-3.5.jar
commons-logging-1.1.3.jar
commons-math3-3.4.1.jar
commons-net-3.1.jar
commons-pool-1.5.4.jar
compress-lzf-1.0.3.jar
core-1.1.2.jar
curator-client-2.7.1.jar
curator-framework-2.7.1.jar
curator-recipes-2.7.1.jar
datanucleus-api-jdo-3.2.6.jar
datanucleus-core-3.2.10.jar
datanucleus-rdbms-3.2.9.jar
derby-10.12.1.1.jar
eigenbase-properties-1.1.5.jar
[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.
2017-07-10 18:21:03 -04:00
flatbuffers-1.2.0-3f79e055.jar
generex-1.0.1.jar
gson-2.2.4.jar
guava-14.0.1.jar
guice-3.0.jar
guice-servlet-3.0.jar
hadoop-annotations-2.7.3.jar
hadoop-auth-2.7.3.jar
hadoop-client-2.7.3.jar
hadoop-common-2.7.3.jar
hadoop-hdfs-2.7.3.jar
hadoop-mapreduce-client-app-2.7.3.jar
hadoop-mapreduce-client-common-2.7.3.jar
hadoop-mapreduce-client-core-2.7.3.jar
hadoop-mapreduce-client-jobclient-2.7.3.jar
hadoop-mapreduce-client-shuffle-2.7.3.jar
hadoop-yarn-api-2.7.3.jar
hadoop-yarn-client-2.7.3.jar
hadoop-yarn-common-2.7.3.jar
hadoop-yarn-server-common-2.7.3.jar
hadoop-yarn-server-web-proxy-2.7.3.jar
hk2-api-2.4.0-b34.jar
hk2-locator-2.4.0-b34.jar
hk2-utils-2.4.0-b34.jar
hppc-0.7.2.jar
htrace-core-3.1.0-incubating.jar
httpclient-4.5.4.jar
httpcore-4.4.8.jar
ivy-2.4.0.jar
jackson-annotations-2.6.7.jar
jackson-core-2.6.7.jar
jackson-core-asl-1.9.13.jar
jackson-databind-2.6.7.1.jar
jackson-dataformat-yaml-2.6.7.jar
jackson-jaxrs-1.9.13.jar
jackson-mapper-asl-1.9.13.jar
jackson-module-jaxb-annotations-2.6.7.jar
jackson-module-paranamer-2.7.9.jar
jackson-module-scala_2.11-2.6.7.1.jar
jackson-xc-1.9.13.jar
janino-3.0.8.jar
java-xmlbuilder-1.1.jar
javassist-3.18.1-GA.jar
javax.annotation-api-1.2.jar
javax.inject-1.jar
javax.inject-2.4.0-b34.jar
javax.servlet-api-3.1.0.jar
javax.ws.rs-api-2.0.1.jar
javolution-5.5.1.jar
jaxb-api-2.2.2.jar
jcl-over-slf4j-1.7.16.jar
jdo-api-3.0.1.jar
jersey-client-2.22.2.jar
jersey-common-2.22.2.jar
jersey-container-servlet-2.22.2.jar
jersey-container-servlet-core-2.22.2.jar
jersey-guava-2.22.2.jar
jersey-media-jaxb-2.22.2.jar
jersey-server-2.22.2.jar
jets3t-0.9.4.jar
jetty-6.1.26.jar
jetty-util-6.1.26.jar
jline-2.12.1.jar
joda-time-2.9.3.jar
jodd-core-3.5.2.jar
jpam-1.1.jar
json4s-ast_2.11-3.5.3.jar
json4s-core_2.11-3.5.3.jar
json4s-jackson_2.11-3.5.3.jar
json4s-scalap_2.11-3.5.3.jar
jsp-api-2.1.jar
jsr305-1.3.9.jar
jta-1.1.jar
jtransforms-2.4.0.jar
jul-to-slf4j-1.7.16.jar
kryo-shaded-3.0.3.jar
kubernetes-client-3.0.0.jar
kubernetes-model-2.0.0.jar
leveldbjni-all-1.8.jar
libfb303-0.9.3.jar
libthrift-0.9.3.jar
log4j-1.2.17.jar
logging-interceptor-3.8.1.jar
lz4-java-1.4.0.jar
machinist_2.11-0.6.1.jar
macro-compat_2.11-1.1.1.jar
[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.
2017-11-29 17:15:35 -05:00
mesos-1.4.0-shaded-protobuf.jar
metrics-core-3.1.5.jar
metrics-graphite-3.1.5.jar
metrics-json-3.1.5.jar
metrics-jvm-3.1.5.jar
minlog-1.3.0.jar
netty-3.9.9.Final.jar
netty-all-4.1.17.Final.jar
objenesis-2.1.jar
okhttp-3.8.1.jar
okio-1.13.0.jar
opencsv-2.3.jar
orc-core-1.4.3-nohive.jar
orc-mapreduce-1.4.3-nohive.jar
oro-2.0.8.jar
osgi-resource-locator-1.0.1.jar
paranamer-2.8.jar
parquet-column-1.10.0.jar
parquet-common-1.10.0.jar
parquet-encoding-1.10.0.jar
parquet-format-2.4.0.jar
parquet-hadoop-1.10.0.jar
parquet-hadoop-bundle-1.6.0.jar
parquet-jackson-1.10.0.jar
protobuf-java-2.5.0.jar
py4j-0.10.7.jar
pyrolite-4.13.jar
scala-compiler-2.11.8.jar
scala-library-2.11.8.jar
scala-parser-combinators_2.11-1.0.4.jar
scala-reflect-2.11.8.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.
2017-09-01 14:21:21 -04:00
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
spire_2.11-0.13.0.jar
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
2017-12-06 16:22:08 -05:00
univocity-parsers-2.5.9.jar
validation-api-1.1.0.Final.jar
xbean-asm5-shaded-4.4.jar
xercesImpl-2.9.1.jar
xmlenc-0.52.jar
xz-1.0.jar
zjsonpatch-0.3.0.jar
zookeeper-3.4.6.jar
zstd-jni-1.3.2-2.jar