Pulling functionality from apache spark #4
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…ing BlockRDD When write ahead log is not enabled, a recovered streaming driver still tries to run jobs using pre-failure block ids, and fails as the block do not exists in-memory any more (and cannot be recovered as receiver WAL is not enabled). This occurs because the driver-side WAL of ReceivedBlockTracker is recovers that past block information, and ReceiveInputDStream creates BlockRDDs even if those blocks do not exist. The solution in this PR is to filter out block ids that do not exist before creating the BlockRDD. In addition, it adds unit tests to verify other logic in ReceiverInputDStream. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #8405 from tdas/SPARK-10210.
We misunderstood the Julian days and nanoseconds of the day in parquet (as TimestampType) from Hive/Impala, they are overlapped, so can't be added together directly. In order to avoid the confusing rounding when do the converting, we use `2440588` as the Julian Day of epoch of unix timestamp (which should be 2440587.5). Author: Davies Liu <davies@databricks.com> Author: Cheng Lian <lian@databricks.com> Closes #8400 from davies/timestamp_parquet.
Spark SQL's data sources API exposes Catalyst's internal types through its Filter interfaces. This is a problem because types like UTF8String are not stable developer APIs and should not be exposed to third-parties. This issue caused incompatibilities when upgrading our `spark-redshift` library to work against Spark 1.5.0. To avoid these issues in the future we should only expose public types through these Filter objects. This patch accomplishes this by using CatalystTypeConverters to add the appropriate conversions. Author: Josh Rosen <joshrosen@databricks.com> Closes #8403 from JoshRosen/datasources-internal-vs-external-types.
…rs). https://issues.apache.org/jira/browse/SPARK-10197 Author: Yin Huai <yhuai@databricks.com> Closes #8407 from yhuai/ORCSPARK-10197.
Author: Zhang, Liye <liye.zhang@intel.com> Closes #8412 from liyezhang556520/minorDoc.
Author: ehnalis <zoltan.zvara@gmail.com> Closes #8308 from ehnalis/master.
…ng uses to JavaConverters Replace `JavaConversions` implicits with `JavaConverters` Most occurrences I've seen so far are necessary conversions; a few have been avoidable. None are in critical code as far as I see, yet. Author: Sean Owen <sowen@cloudera.com> Closes #8033 from srowen/SPARK-9613.
Author: Michael Armbrust <michael@databricks.com> Closes #8404 from marmbrus/turnOffPartitionVerification.
jira: https://issues.apache.org/jira/browse/SPARK-8531 Update ML user guide for MinMaxScaler Author: Yuhao Yang <hhbyyh@gmail.com> Author: unknown <yuhaoyan@yuhaoyan-MOBL1.ccr.corp.intel.com> Closes #7211 from hhbyyh/minmaxdoc.
See [discussion](#8254 (comment)) CC jkbradley Author: Feynman Liang <fliang@databricks.com> Closes #8422 from feynmanliang/SPARK-10230.
Update `Since` annotation in `mllib.classification`: 1. add version to classes, objects, constructors, and public variables declared in constructors 2. correct some versions 3. remove `Since` on `toString` MechCoder dbtsai Author: Xiangrui Meng <meng@databricks.com> Closes #8421 from mengxr/SPARK-10231 and squashes the following commits: b2dce80 [Xiangrui Meng] update @SInCE annotation for mllib.classification
This PR: 1. supports transferring arbitrary nested array from JVM to R side in SerDe; 2. based on 1, collect() implemenation is improved. Now it can support collecting data of complex types from a DataFrame. Author: Sun Rui <rui.sun@intel.com> Closes #8276 from sun-rui/SPARK-10048.
* Adds doc for alias of runMIniBatchSGD documenting default value for convergeTol * Cleans up a note in code Author: Feynman Liang <fliang@databricks.com> Closes #8425 from feynmanliang/SPARK-9800.
Same as #8421 but for `mllib.fpm`. cc feynmanliang Author: Xiangrui Meng <meng@databricks.com> Closes #8429 from mengxr/SPARK-10237.
…genceTol default value Adds default convergence tolerance (0.001, set in `GradientDescent.convergenceTol`) to `setConvergenceTol`'s scaladoc Author: Feynman Liang <fliang@databricks.com> Closes #8424 from feynmanliang/SPARK-9797.
…ml and mllib.util Same as #8421 but for `mllib.pmml` and `mllib.util`. cc dbtsai Author: Xiangrui Meng <meng@databricks.com> Closes #8430 from mengxr/SPARK-10239 and squashes the following commits: a189acf [Xiangrui Meng] update since versions in mllib.pmml and mllib.util
In BigDecimal or java.math.BigDecimal, the precision could be smaller than scale, for example, BigDecimal("0.001") has precision = 1 and scale = 3. But DecimalType require that the precision should be larger than scale, so we should use the maximum of precision and scale when inferring the schema from decimal literal.
Author: Davies Liu <davies@databricks.com>
Closes #8428 from davies/smaller_decimal.
Follow the rule in Hive for decimal division. see https://github.com/apache/hive/blob/ac755ebe26361a4647d53db2a28500f71697b276/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDFOPDivide.java#L113 cc chenghao-intel Author: Davies Liu <davies@databricks.com> Closes #8415 from davies/decimal_div2.
* Adds two new sections to LDA's user guide; one for each optimizer/model * Documents new features added to LDA (e.g. topXXXperXXX, asymmetric priors, hyperpam optimization) * Cleans up a TODO and sets a default parameter in LDA code jkbradley hhbyyh Author: Feynman Liang <fliang@databricks.com> Closes #8254 from feynmanliang/SPARK-9888.
Same as #8421 but for `mllib.evaluation`. cc avulanov Author: Xiangrui Meng <meng@databricks.com> Closes #8423 from mengxr/SPARK-10233.
Same as #8421 but for `mllib.linalg`. cc dbtsai Author: Xiangrui Meng <meng@databricks.com> Closes #8440 from mengxr/SPARK-10238 and squashes the following commits: b38437e [Xiangrui Meng] update since versions in mllib.linalg
…ndom and mllib.stat The same as #8241 but for `mllib.stat` and `mllib.random`. cc feynmanliang Author: Xiangrui Meng <meng@databricks.com> Closes #8439 from mengxr/SPARK-10242.
Same as #8421 but for `mllib.clustering`. cc feynmanliang yu-iskw Author: Xiangrui Meng <meng@databricks.com> Closes #8435 from mengxr/SPARK-10234.
Same as #8421 but for `mllib.tree`. cc jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #8442 from mengxr/SPARK-10236.
Same as #8421 but for `mllib.regression`. cc freeman-lab dbtsai Author: Xiangrui Meng <meng@databricks.com> Closes #8426 from mengxr/SPARK-10235 and squashes the following commits: 6cd28e4 [Xiangrui Meng] update since versions in mllib.regression
Same as #8421 but for `mllib.feature`. cc dbtsai Author: Xiangrui Meng <meng@databricks.com> Closes #8449 from mengxr/SPARK-10236.feature and squashes the following commits: 0e8d658 [Xiangrui Meng] remove unnecessary comment ad70b03 [Xiangrui Meng] update since versions in mllib.feature
…r filter / select) Add support for ``` df[df$name == "Smith", c(1,2)] df[df$age %in% c(19, 30), 1:2] ``` shivaram Author: felixcheung <felixcheung_m@hotmail.com> Closes #8394 from felixcheung/rsubset.
I only found `ml.NaiveBayes` missing `Experimental` annotation. This PR doesn't cover Python APIs. cc jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #8452 from mengxr/SPARK-9665.
Links work now properly + consistent use of *Spark standalone cluster* (Spark uppercase + lowercase the rest -- seems agreed in the other places in the docs). Author: Jacek Laskowski <jacek.laskowski@deepsense.io> Closes #8759 from jaceklaskowski/docs-submitting-apps.
Comments preceding toMessage method state: "The edge partition is encoded in the lower * 30 bytes of the Int, and the position is encoded in the upper 2 bytes of the Int.". References to bytes should be changed to bits. This contribution is my original work and I license the work to the Spark project under it's open source license. Author: Robin East <robin.east@xense.co.uk> Closes #8756 from insidedctm/master.
Author: Reynold Xin <rxin@databricks.com> Closes #8350 from rxin/1.6.
jira: https://issues.apache.org/jira/browse/SPARK-10491 We implemented dspr with sparse vector support in `RowMatrix`. This method is also used in WeightedLeastSquares and other places. It would be useful to move it to `linalg.BLAS`. Let me know if new UT needed. Author: Yuhao Yang <hhbyyh@gmail.com> Closes #8663 from hhbyyh/movedspr.
This change does two things: - tag a few tests and adds the mechanism in the build to be able to disable those tags, both in maven and sbt, for both junit and scalatest suites. - add some logic to run-tests.py to disable some tags depending on what files have changed; that's used to disable expensive tests when a module hasn't explicitly been changed, to speed up testing for changes that don't directly affect those modules. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #8437 from vanzin/test-tags.
…ib.random Missed this when reviewing `pyspark.mllib.random` for SPARK-10275. Author: noelsmith <mail@noelsmith.com> Closes #8773 from noel-smith/mllib-random-versionadded-fix.
…n-tests.py." This reverts commit 8abef21.
JIRA: https://issues.apache.org/jira/browse/SPARK-10437 If an expression in `SortOrder` is a resolved one, such as `count(1)`, the corresponding rule in `Analyzer` to make it work in order by will not be applied. Author: Liang-Chi Hsieh <viirya@appier.com> Closes #8599 from viirya/orderby-agg.
Sometimes we can't push down the whole `Project` though `Sort`, but we still have a chance to push down part of it. Author: Wenchen Fan <cloud0fan@outlook.com> Closes #8644 from cloud-fan/column-prune.
…ession In fraud detection dataset, almost all the samples are negative while only couple of them are positive. This type of high imbalanced data will bias the models toward negative resulting poor performance. In python-scikit, they provide a correction allowing users to Over-/undersample the samples of each class according to the given weights. In auto mode, selects weights inversely proportional to class frequencies in the training set. This can be done in a more efficient way by multiplying the weights into loss and gradient instead of doing actual over/undersampling in the training dataset which is very expensive. http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html On the other hand, some of the training data maybe more important like the training samples from tenure users while the training samples from new users maybe less important. We should be able to provide another "weight: Double" information in the LabeledPoint to weight them differently in the learning algorithm. Author: DB Tsai <dbt@netflix.com> Author: DB Tsai <dbt@dbs-mac-pro.corp.netflix.com> Closes #7884 from dbtsai/SPARK-7685.
*Note: this is for master branch only.* The fix for branch-1.5 is at #8721. The query execution ID is currently passed from a thread to its children, which is not the intended behavior. This led to `IllegalArgumentException: spark.sql.execution.id is already set` when running queries in parallel, e.g.: ``` (1 to 100).par.foreach { _ => sc.parallelize(1 to 5).map { i => (i, i) }.toDF("a", "b").count() } ``` The cause is `SparkContext`'s local properties are inherited by default. This patch adds a way to exclude keys we don't want to be inherited, and makes SQL go through that code path. Author: Andrew Or <andrew@databricks.com> Closes #8710 from andrewor14/concurrent-sql-executions.
The idea is that we should separate the function call that does memory reservation (i.e. prepare) from the function call that consumes the input (e.g. open()), so all operators can be a chance to reserve memory before they are all consumed. Author: Reynold Xin <rxin@databricks.com> Closes #8761 from rxin/SPARK-10612.
Remove return statements in RDD.takeSample and wrap it withScope Author: vinodkc <vinod.kc.in@gmail.com> Author: vinodkc <vinodkc@users.noreply.github.com> Author: Vinod K C <vinod.kc@huawei.com> Closes #8730 from vinodkc/fix_takesample_return.
…mitCoordinator When speculative execution is enabled, consider a scenario where the authorized committer of a particular output partition fails during the OutputCommitter.commitTask() call. In this case, the OutputCommitCoordinator is supposed to release that committer's exclusive lock on committing once that task fails. However, due to a unit mismatch (we used task attempt number in one place and task attempt id in another) the lock will not be released, causing Spark to go into an infinite retry loop. This bug was masked by the fact that the OutputCommitCoordinator does not have enough end-to-end tests (the current tests use many mocks). Other factors contributing to this bug are the fact that we have many similarly-named identifiers that have different semantics but the same data types (e.g. attemptNumber and taskAttemptId, with inconsistent variable naming which makes them difficult to distinguish). This patch adds a regression test and fixes this bug by always using task attempt numbers throughout this code. Author: Josh Rosen <joshrosen@databricks.com> Closes #8544 from JoshRosen/SPARK-10381.
…n SQLContext Instead of relying on `DataFrames` to verify our answers, we can just use simple arrays. This significantly simplifies the test logic for `LocalNode`s and reduces a lot of code duplicated from `SparkPlanTest`. This also fixes an additional issue [SPARK-10624](https://issues.apache.org/jira/browse/SPARK-10624) where the output of `TakeOrderedAndProjectNode` is not actually ordered. Author: Andrew Or <andrew@databricks.com> Closes #8764 from andrewor14/sql-local-tests-cleanup.
…check the table. Current implementation uses query with a LIMIT clause to find if table already exists. This syntax works only in some database systems. This patch changes the default query to the one that is likely to work on most databases, and adds a new method to the JdbcDialect abstract class to allow dialects to override the default query. I looked at using the JDBC meta data calls, it turns out there is no common way to find the current schema, catalog..etc. There is a new method Connection.getSchema() , but that is available only starting jdk1.7 , and existing jdbc drivers may not have implemented it. Other option was to use jdbc escape syntax clause for LIMIT, not sure on how well this supported in all the databases also. After looking at all the jdbc metadata options my conclusion was most common way is to use the simple select query with 'where 1 =0' , and allow dialects to customize as needed Author: sureshthalamati <suresh.thalamati@gmail.com> Closes #8676 from sureshthalamati/table_exists_spark-9078.
Various ML guide cleanups. * ml-guide.md: Make it easier to access the algorithm-specific guides. * LDA user guide: EM often begins with useless topics, but running longer generally improves them dramatically. E.g., 10 iterations on a Wikipedia dataset produces useless topics, but 50 iterations produces very meaningful topics. * mllib-feature-extraction.html#elementwiseproduct: “w” parameter should be “scalingVec” * Clean up Binarizer user guide a little. * Document in Pipeline that users should not put an instance into the Pipeline in more than 1 place. * spark.ml Word2Vec user guide: clean up grammar/writing * Chi Sq Feature Selector docs: Improve text in doc. CC: mengxr feynmanliang Author: Joseph K. Bradley <joseph@databricks.com> Closes #8752 from jkbradley/mlguide-fixes-1.5.
Author: Vinod K C <vinod.kc@huawei.com> Closes #8682 from vinodkc/fix_SPARK-10516.
…stro The calculation of Spark version is downloading Scala and Zinc in the build directory which is inflating the size of the source distribution. Reseting the repo before packaging the source distribution fix this issue. Author: Luciano Resende <lresende@apache.org> Closes #8774 from lresende/spark-10511.
…b.recommendation Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #8677 from yu-iskw/SPARK-10276.
Set `X-Frame-Options: SAMEORIGIN` to protect against frame-related vulnerability Author: Sean Owen <sowen@cloudera.com> Closes #8745 from srowen/SPARK-10589.
1. Support collecting data of MapType from DataFrame. 2. Support data of MapType in createDataFrame. Author: Sun Rui <rui.sun@intel.com> Closes #8711 from sun-rui/SPARK-10050.
This fixes https://issues.apache.org/jira/browse/SPARK-9794 by using a real ISO8601 parser. (courtesy of the xml component of the standard java library) cc: angelini Author: Kevin Cox <kevincox@kevincox.ca> Closes #8396 from kevincox/kevincox-sql-time-parsing.
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Pulling functionality from apache spark
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…l` in IF/CASEWHEN
## What changes were proposed in this pull request?
Currently, `SimplifyConditionals` handles `true` and `false` to optimize branches. This PR improves `SimplifyConditionals` to take advantage of `null` conditions for `if` and `CaseWhen` expressions, too.
**Before**
```
scala> sql("SELECT IF(null, 1, 0)").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [if (null) 1 else 0 AS (IF(CAST(NULL AS BOOLEAN), 1, 0))#4]
: +- INPUT
+- Scan OneRowRelation[]
scala> sql("select case when cast(null as boolean) then 1 else 2 end").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [CASE WHEN null THEN 1 ELSE 2 END AS CASE WHEN CAST(NULL AS BOOLEAN) THEN 1 ELSE 2 END#14]
: +- INPUT
+- Scan OneRowRelation[]
```
**After**
```
scala> sql("SELECT IF(null, 1, 0)").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [0 AS (IF(CAST(NULL AS BOOLEAN), 1, 0))#4]
: +- INPUT
+- Scan OneRowRelation[]
scala> sql("select case when cast(null as boolean) then 1 else 2 end").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [2 AS CASE WHEN CAST(NULL AS BOOLEAN) THEN 1 ELSE 2 END#4]
: +- INPUT
+- Scan OneRowRelation[]
```
**Hive**
```
hive> select if(null,1,2);
OK
2
hive> select case when cast(null as boolean) then 1 else 2 end;
OK
2
```
## How was this patch tested?
Pass the Jenkins tests (including new extended test cases).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes apache#12122 from dongjoon-hyun/SPARK-14338.
rekhajoshm
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…pressions
## What changes were proposed in this pull request?
This PR changes the direction of expression transformation in the DecimalPrecision rule. Previously, the expressions were transformed down, which led to incorrect result types when decimal expressions had other decimal expressions as their operands. The root cause of this issue was in visiting outer nodes before their children. Consider the example below:
```
val inputSchema = StructType(StructField("col", DecimalType(26, 6)) :: Nil)
val sc = spark.sparkContext
val rdd = sc.parallelize(1 to 2).map(_ => Row(BigDecimal(12)))
val df = spark.createDataFrame(rdd, inputSchema)
// Works correctly since no nested decimal expression is involved
// Expected result type: (26, 6) * (26, 6) = (38, 12)
df.select($"col" * $"col").explain(true)
df.select($"col" * $"col").printSchema()
// Gives a wrong result since there is a nested decimal expression that should be visited first
// Expected result type: ((26, 6) * (26, 6)) * (26, 6) = (38, 12) * (26, 6) = (38, 18)
df.select($"col" * $"col" * $"col").explain(true)
df.select($"col" * $"col" * $"col").printSchema()
```
The example above gives the following output:
```
// Correct result without sub-expressions
== Parsed Logical Plan ==
'Project [('col * 'col) AS (col * col)#4]
+- LogicalRDD [col#1]
== Analyzed Logical Plan ==
(col * col): decimal(38,12)
Project [CheckOverflow((promote_precision(cast(col#1 as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) AS (col * col)#4]
+- LogicalRDD [col#1]
== Optimized Logical Plan ==
Project [CheckOverflow((col#1 * col#1), DecimalType(38,12)) AS (col * col)#4]
+- LogicalRDD [col#1]
== Physical Plan ==
*Project [CheckOverflow((col#1 * col#1), DecimalType(38,12)) AS (col * col)#4]
+- Scan ExistingRDD[col#1]
// Schema
root
|-- (col * col): decimal(38,12) (nullable = true)
// Incorrect result with sub-expressions
== Parsed Logical Plan ==
'Project [(('col * 'col) * 'col) AS ((col * col) * col)#11]
+- LogicalRDD [col#1]
== Analyzed Logical Plan ==
((col * col) * col): decimal(38,12)
Project [CheckOverflow((promote_precision(cast(CheckOverflow((promote_precision(cast(col#1 as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) AS ((col * col) * col)#11]
+- LogicalRDD [col#1]
== Optimized Logical Plan ==
Project [CheckOverflow((cast(CheckOverflow((col#1 * col#1), DecimalType(38,12)) as decimal(26,6)) * col#1), DecimalType(38,12)) AS ((col * col) * col)#11]
+- LogicalRDD [col#1]
== Physical Plan ==
*Project [CheckOverflow((cast(CheckOverflow((col#1 * col#1), DecimalType(38,12)) as decimal(26,6)) * col#1), DecimalType(38,12)) AS ((col * col) * col)#11]
+- Scan ExistingRDD[col#1]
// Schema
root
|-- ((col * col) * col): decimal(38,12) (nullable = true)
```
## How was this patch tested?
This PR was tested with available unit tests. Moreover, there are tests to cover previously failing scenarios.
Author: aokolnychyi <anton.okolnychyi@sap.com>
Closes apache#18583 from aokolnychyi/spark-21332.
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