The SQL programming guide's link to the DataFrame functions reference points to the wrong location; this patch fixes that.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9269 from JoshRosen/SPARK-11299.
Two typos squashed.
BTW Let me know how to proceed with other typos if I ran across any. I don't feel well to leave them aside as much as sending pull requests with such tiny changes. Guide me.
Author: Jacek Laskowski <jacek.laskowski@deepsense.io>
Closes#9250 from jaceklaskowski/typos-hunting.
Temporarily remove GREP_OPTIONS if set in bin/spark-class.
Some GREP_OPTIONS will modify the output of the grep commands that are looking for the assembly jars.
For example, if the -n option is specified, the grep output will look like:
5:spark-assembly-1.5.1-hadoop2.4.0.jar
This will not match the regular expressions, and so the jar files will not be found. We could improve the regular expression to handle this case and trim off extra characters, but it is difficult to know which options may or may not be set. Unsetting GREP_OPTIONS within the script handles all the cases and gives the desired output.
Author: Jeffrey Naisbitt <jnaisbitt@familysearch.org>
Closes#9231 from naisbitt/unset-GREP_OPTIONS.
…ut building with -Phive-thriftserver and SPARK_PREPEND_CLASSES is set
This is the exception after this patch. Please help review.
```
java.lang.NoClassDefFoundError: org/apache/hadoop/hive/cli/CliDriver
at java.lang.ClassLoader.defineClass1(Native Method)
at java.lang.ClassLoader.defineClass(ClassLoader.java:800)
at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
at java.net.URLClassLoader.defineClass(URLClassLoader.java:449)
at java.net.URLClassLoader.access$100(URLClassLoader.java:71)
at java.net.URLClassLoader$1.run(URLClassLoader.java:361)
at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:308)
at java.lang.ClassLoader.loadClass(ClassLoader.java:412)
at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:270)
at org.apache.spark.util.Utils$.classForName(Utils.scala:173)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:647)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:120)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.ClassNotFoundException: org.apache.hadoop.hive.cli.CliDriver
at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:308)
at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
... 21 more
Failed to load hive class.
You need to build Spark with -Phive and -Phive-thriftserver.
```
Author: Jeff Zhang <zjffdu@apache.org>
Closes#9134 from zjffdu/SPARK-11125.
Add examples for read.df, write.df; fix grouping for read.df, loadDF; fix formatting and text truncation for write.df, saveAsTable.
Several text issues:
- text collapsed into a single paragraph
- text truncated at 2 places, eg. "overwrite: Existing data is expected to be overwritten by the contents of error:"
shivaram
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#9261 from felixcheung/rdocreadwritedf.
This adds API for reading and writing text files, similar to SparkContext.textFile and RDD.saveAsTextFile.
```
SQLContext.read.text("/path/to/something.txt")
DataFrame.write.text("/path/to/write.txt")
```
Using the new Dataset API, this also supports
```
val ds: Dataset[String] = SQLContext.read.text("/path/to/something.txt").as[String]
```
Author: Reynold Xin <rxin@databricks.com>
Closes#9240 from rxin/SPARK-11274.
This is a PR for Parquet-based model import/export.
* Added save/load for ChiSqSelectorModel
* Updated the test suite ChiSqSelectorSuite
Author: Jayant Shekar <jayant@user-MBPMBA-3.local>
Closes#6785 from jayantshekhar/SPARK-6723.
A POC code for making example code in user guide testable.
mengxr We still need to talk about the labels in code.
Author: Xusen Yin <yinxusen@gmail.com>
Closes#9109 from yinxusen/SPARK-10382.
For nested StructType, the underline buffer could be used for others before, we should zero out the padding bytes for those primitive types that have less than 8 bytes.
cc cloud-fan
Author: Davies Liu <davies@databricks.com>
Closes#9217 from davies/zero_out.
This test can take a little while to finish on slow / loaded machines.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#9235 from vanzin/SPARK-11134.
The current NettyRpc has a message order issue because it uses a thread pool to send messages. E.g., running the following two lines in the same thread,
```
ref.send("A")
ref.send("B")
```
The remote endpoint may see "B" before "A" because sending "A" and "B" are in parallel.
To resolve this issue, this PR added an outbox for each connection, and if we are connecting to the remote node when sending messages, just cache the sending messages in the outbox and send them one by one when the connection is established.
Author: zsxwing <zsxwing@gmail.com>
Closes#9197 from zsxwing/rpc-outbox.
```
// My machine only has 8 cores
$ bin/spark-shell --master local[32]
scala> val df = sc.parallelize(Seq((1, 1), (2, 2))).toDF("a", "b")
scala> df.as("x").join(df.as("y"), $"x.a" === $"y.a").count()
Caused by: java.io.IOException: Unable to acquire 2097152 bytes of memory
at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:351)
```
Author: Andrew Or <andrew@databricks.com>
Closes#9209 from andrewor14/fix-local-page-size.
*This PR adds a new experimental API to Spark, tentitively named Datasets.*
A `Dataset` is a strongly-typed collection of objects that can be transformed in parallel using functional or relational operations. Example usage is as follows:
### Functional
```scala
> val ds: Dataset[Int] = Seq(1, 2, 3).toDS()
> ds.filter(_ % 1 == 0).collect()
res1: Array[Int] = Array(1, 2, 3)
```
### Relational
```scala
scala> ds.toDF().show()
+-----+
|value|
+-----+
| 1|
| 2|
| 3|
+-----+
> ds.select(expr("value + 1").as[Int]).collect()
res11: Array[Int] = Array(2, 3, 4)
```
## Comparison to RDDs
A `Dataset` differs from an `RDD` in the following ways:
- The creation of a `Dataset` requires the presence of an explicit `Encoder` that can be
used to serialize the object into a binary format. Encoders are also capable of mapping the
schema of a given object to the Spark SQL type system. In contrast, RDDs rely on runtime
reflection based serialization.
- Internally, a `Dataset` is represented by a Catalyst logical plan and the data is stored
in the encoded form. This representation allows for additional logical operations and
enables many operations (sorting, shuffling, etc.) to be performed without deserializing to
an object.
A `Dataset` can be converted to an `RDD` by calling the `.rdd` method.
## Comparison to DataFrames
A `Dataset` can be thought of as a specialized DataFrame, where the elements map to a specific
JVM object type, instead of to a generic `Row` container. A DataFrame can be transformed into
specific Dataset by calling `df.as[ElementType]`. Similarly you can transform a strongly-typed
`Dataset` to a generic DataFrame by calling `ds.toDF()`.
## Implementation Status and TODOs
This is a rough cut at the least controversial parts of the API. The primary purpose here is to get something committed so that we can better parallelize further work and get early feedback on the API. The following is being deferred to future PRs:
- Joins and Aggregations (prototype here f11f91e6f0)
- Support for Java
Additionally, the responsibility for binding an encoder to a given schema is currently done in a fairly ad-hoc fashion. This is an internal detail, and what we are doing today works for the cases we care about. However, as we add more APIs we'll probably need to do this in a more principled way (i.e. separate resolution from binding as we do in DataFrames).
## COMPATIBILITY NOTE
Long term we plan to make `DataFrame` extend `Dataset[Row]`. However,
making this change to che class hierarchy would break the function signatures for the existing
function operations (map, flatMap, etc). As such, this class should be considered a preview
of the final API. Changes will be made to the interface after Spark 1.6.
Author: Michael Armbrust <michael@databricks.com>
Closes#9190 from marmbrus/dataset-infra.
To enable the unit test of `hadoopFsRelationSuite.Partition column type casting`. It previously threw exception like below, as we treat the auto infer partition schema with higher priority than the user specified one.
```
java.lang.ClassCastException: java.lang.Integer cannot be cast to org.apache.spark.unsafe.types.UTF8String
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow$class.getUTF8String(rows.scala:45)
at org.apache.spark.sql.catalyst.expressions.GenericInternalRow.getUTF8String(rows.scala:220)
at org.apache.spark.sql.catalyst.expressions.JoinedRow.getUTF8String(JoinedRow.scala:102)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(generated.java:62)
at org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$17$$anonfun$apply$9.apply(DataSourceStrategy.scala:212)
at org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$17$$anonfun$apply$9.apply(DataSourceStrategy.scala:212)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:903)
at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:903)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1846)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1846)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:88)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
07:44:01.344 ERROR org.apache.spark.executor.Executor: Exception in task 14.0 in stage 3.0 (TID 206)
java.lang.ClassCastException: java.lang.Integer cannot be cast to org.apache.spark.unsafe.types.UTF8String
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow$class.getUTF8String(rows.scala:45)
at org.apache.spark.sql.catalyst.expressions.GenericInternalRow.getUTF8String(rows.scala:220)
at org.apache.spark.sql.catalyst.expressions.JoinedRow.getUTF8String(JoinedRow.scala:102)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(generated.java:62)
at org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$17$$anonfun$apply$9.apply(DataSourceStrategy.scala:212)
at org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$17$$anonfun$apply$9.apply(DataSourceStrategy.scala:212)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:903)
at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:903)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1846)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1846)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:88)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
```
Author: Cheng Hao <hao.cheng@intel.com>
Closes#8026 from chenghao-intel/partition_discovery.
This commit removes unnecessary calls to addPendingTask in
TaskSetManager.executorLost. These calls are unnecessary: for
tasks that are still pending and haven't been launched, they're
still in all of the correct pending lists, so calling addPendingTask
has no effect. For tasks that are currently running (which may still be
in the pending lists, depending on how they were scheduled), we call
addPendingTask in handleFailedTask, so the calls at the beginning
of executorLost are redundant.
I think these calls are left over from when we re-computed the locality
levels in addPendingTask; now that we call recomputeLocality separately,
I don't think these are necessary.
Now that those calls are removed, the readding parameter in addPendingTask
is no longer necessary, so this commit also removes that parameter.
markhamstra can you take a look at this?
cc vanzin
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#9154 from kayousterhout/SPARK-11163.
The current `NettyRpcEndpointRef.send` can be interrupted because it uses `LinkedBlockingQueue.put`, which may hang the application.
Image the following execution order:
| thread 1: TaskRunner.kill | thread 2: TaskRunner.run
------------- | ------------- | -------------
1 | killed = true |
2 | | if (killed) {
3 | | throw new TaskKilledException
4 | | case _: TaskKilledException _: InterruptedException if task.killed =>
5 | task.kill(interruptThread): interruptThread is true |
6 | | execBackend.statusUpdate(taskId, TaskState.KILLED, ser.serialize(TaskKilled))
7 | | localEndpoint.send(StatusUpdate(taskId, state, serializedData)): in LocalBackend
Then `localEndpoint.send(StatusUpdate(taskId, state, serializedData))` will throw `InterruptedException`. This will prevent the executor from updating the task status and hang the application.
An failure caused by the above issue here: https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/44062/consoleFull
Since `receivers` is an unbounded `LinkedBlockingQueue`, we can just use `LinkedBlockingQueue.offer` to resolve this issue.
Author: zsxwing <zsxwing@gmail.com>
Closes#9198 from zsxwing/dont-interrupt-send.
There's a lot of duplication between SortShuffleManager and UnsafeShuffleManager. Given that these now provide the same set of functionality, now that UnsafeShuffleManager supports large records, I think that we should replace SortShuffleManager's serialized shuffle implementation with UnsafeShuffleManager's and should merge the two managers together.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8829 from JoshRosen/consolidate-sort-shuffle-implementations.
SparkR should remove `.sparkRSQLsc` and `.sparkRHivesc` when `sparkR.stop()` is called. Otherwise even when SparkContext is reinitialized, `sparkRSQL.init` returns the stale copy of the object and complains:
```r
sc <- sparkR.init("local")
sqlContext <- sparkRSQL.init(sc)
sparkR.stop()
sc <- sparkR.init("local")
sqlContext <- sparkRSQL.init(sc)
sqlContext
```
producing
```r
Error in callJMethod(x, "getClass") :
Invalid jobj 1. If SparkR was restarted, Spark operations need to be re-executed.
```
I have added the check and removal only when SparkContext itself is initialized. I have also added corresponding test for this fix. Let me know if you want me to move the test to SQL test suite instead.
p.s. I tried lint-r but ended up a lots of errors on existing code.
Author: Forest Fang <forest.fang@outlook.com>
Closes#9205 from saurfang/sparkR.stop.
Correct the logic to return `HDFSCacheTaskLocation` instance when the input `str` is a in memory location.
Author: zhichao.li <zhichao.li@intel.com>
Closes#9096 from zhichao-li/uselessBranch.
This PR change InMemoryTableScan to output UnsafeRow, and optimize the unrolling and scanning by coping the bytes for var-length types between UnsafeRow and ByteBuffer directly without creating the wrapper objects. When scanning the decimals in TPC-DS store_sales table, it's 80% faster (copy it as long without create Decimal objects).
Author: Davies Liu <davies@databricks.com>
Closes#9203 from davies/unsafe_cache.
I was looking at this code and found the documentation to be insufficient. I added more documentation, and refactored some relevant code path slightly to improve encapsulation. There are more that I want to do, but I want to get these changes in before doing more work.
My goal is to reduce exposing internal fields directly in ShuffleMapStage to improve encapsulation. After this change, DAGScheduler no longer directly writes outputLocs. There are still 3 places that reads outputLocs directly, but we can change those later.
Author: Reynold Xin <rxin@databricks.com>
Closes#9175 from rxin/stage-cleanup.
Macro in hive (which is GenericUDFMacro) contains real function inside of it but it's not conveyed to tasks, resulting null-pointer exception.
Author: navis.ryu <navis@apache.org>
Closes#8354 from navis/SPARK-10151.
In the analysis phase , while processing the rules for IN predicate, we
compare the in-list types to the lhs expression type and generate
cast operation if necessary. In the case of NULL [NOT] IN expr1 , we end up
generating cast between in list types to NULL like cast (1 as NULL) which
is not a valid cast.
The fix is to find a common type between LHS and RHS expressions and cast
all the expression to the common type.
Author: Dilip Biswal <dbiswal@us.ibm.com>
This patch had conflicts when merged, resolved by
Committer: Michael Armbrust <michael@databricks.com>
Closes#9036 from dilipbiswal/spark_8654_new.
The executionHive assumed to be a standard meta store located in temporary directory as a derby db. But hive.metastore.rawstore.impl was not filtered out so any custom implementation of the metastore with other storage properties (not JDO) will persist that temporary functions. CassandraHiveMetaStore from DataStax Enterprise is one of examples.
Author: Artem Aliev <artem.aliev@datastax.com>
Closes#9178 from artem-aliev/SPARK-11208.
I am changing the default behavior of `First`/`Last` to respect null values (the SQL standard default behavior).
https://issues.apache.org/jira/browse/SPARK-9740
Author: Yin Huai <yhuai@databricks.com>
Closes#8113 from yhuai/firstLast.
This PR introduce a new feature to run SQL directly on files without create a table, for example:
```
select id from json.`path/to/json/files` as j
```
Author: Davies Liu <davies@databricks.com>
Closes#9173 from davies/source.
Find out the missing attributes by recursively looking
at the sort order expression and rest of the code
takes care of projecting them out.
Added description from cloud-fan
I wanna explain a bit more about this bug.
When we resolve sort ordering, we will use a special method, which only resolves UnresolvedAttributes and UnresolvedExtractValue. However, for something like Floor('a), even the 'a is resolved, the floor expression may still being unresolved as data type mismatch(for example, 'a is string type and Floor need double type), thus can't pass this filter, and we can't push down this missing attribute 'a
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#9123 from dilipbiswal/SPARK-10534.
Implement encode/decode for external row based on `ClassEncoder`.
TODO:
* code cleanup
* ~~fix corner cases~~
* refactor the encoder interface
* improve test for product codegen, to cover more corner cases.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9184 from cloud-fan/encoder.
Push conjunctive predicates though Aggregate operators when their references are a subset of the groupingExpressions.
Query plan before optimisation :-
Filter ((c#138L = 2) && (a#0 = 3))
Aggregate [a#0], [a#0,count(b#1) AS c#138L]
Project [a#0,b#1]
LocalRelation [a#0,b#1,c#2]
Query plan after optimisation :-
Filter (c#138L = 2)
Aggregate [a#0], [a#0,count(b#1) AS c#138L]
Filter (a#0 = 3)
Project [a#0,b#1]
LocalRelation [a#0,b#1,c#2]
Author: nitin goyal <nitin.goyal@guavus.com>
Author: nitin.goyal <nitin.goyal@guavus.com>
Closes#9167 from nitin2goyal/master.
Currently the documentation for `lit` is inconsistent with doc format, references "Scala symbol" and has no example. Fixing that.
shivaram
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#9187 from felixcheung/rlit.
Due to PARQUET-251, `BINARY` columns in existing Parquet files may be written with corrupted statistics information. This information is used by filter push-down optimization. Since Spark 1.5 turns on Parquet filter push-down by default, we may end up with wrong query results. PARQUET-251 has been fixed in parquet-mr 1.8.1, but Spark 1.5 is still using 1.7.0.
This affects all Spark SQL data types that can be mapped to Parquet {{BINARY}}, namely:
- `StringType`
- `BinaryType`
- `DecimalType`
(But Spark SQL doesn't support pushing down filters involving `DecimalType` columns for now.)
To avoid wrong query results, we should disable filter push-down for columns of `StringType` and `BinaryType` until we upgrade to parquet-mr 1.8.
Author: Cheng Lian <lian@databricks.com>
Closes#9152 from liancheng/spark-11153.workaround-parquet-251.
(cherry picked from commit 0887e5e878)
Signed-off-by: Cheng Lian <lian@databricks.com>
Namely "." shows up in some places in the template when using the param docstring and not in others
Author: Holden Karau <holden@pigscanfly.ca>
Closes#9017 from holdenk/SPARK-10767-Make-pyspark-shared-params-codegen-more-consistent.
Given row_ind should be less than the number of rows
Given col_ind should be less than the number of cols.
The current code in master gives unpredictable behavior for such cases.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#8271 from MechCoder/hash_code_matrices.
