f49284b5bf
This patch adds managed-memory-based aggregation to Spark SQL / DataFrames. Instead of working with Java objects, this new aggregation path uses `sun.misc.Unsafe` to manipulate raw memory. This reduces the memory footprint for aggregations, resulting in fewer spills, OutOfMemoryErrors, and garbage collection pauses. As a result, this allows for higher memory utilization. It can also result in better cache locality since objects will be stored closer together in memory. This feature can be eanbled by setting `spark.sql.unsafe.enabled=true`. For now, this feature is only supported when codegen is enabled and only supports aggregations for which the grouping columns are primitive numeric types or strings and aggregated values are numeric. ### Managing memory with sun.misc.Unsafe This patch supports both on- and off-heap managed memory. - In on-heap mode, memory addresses are identified by the combination of a base Object and an offset within that object. - In off-heap mode, memory is addressed directly with 64-bit long addresses. To support both modes, functions that manipulate memory accept both `baseObject` and `baseOffset` fields. In off-heap mode, we simply pass `null` as `baseObject`. We allocate memory in large chunks, so memory fragmentation and allocation speed are not significant bottlenecks. By default, we use on-heap mode. To enable off-heap mode, set `spark.unsafe.offHeap=true`. To track allocated memory, this patch extends `SparkEnv` with an `ExecutorMemoryManager` and supplies each `TaskContext` with a `TaskMemoryManager`. These classes work together to track allocations and detect memory leaks. ### Compact tuple format This patch introduces `UnsafeRow`, a compact row layout. In this format, each tuple has three parts: a null bit set, fixed length values, and variable-length values:  - Rows are always 8-byte word aligned (so their sizes will always be a multiple of 8 bytes) - The bit set is used for null tracking: - Position _i_ is set if and only if field _i_ is null - The bit set is aligned to an 8-byte word boundary. - Every field appears as an 8-byte word in the fixed-length values part: - If a field is null, we zero out the values. - If a field is variable-length, the word stores a relative offset (w.r.t. the base of the tuple) that points to the beginning of the field's data in the variable-length part. - Each variable-length data type can have its own encoding: - For strings, the first word stores the length of the string and is followed by UTF-8 encoded bytes. If necessary, the end of the string is padded with empty bytes in order to ensure word-alignment. For example, a tuple that consists 3 fields of type (int, string, string), with value (null, “data”, “bricks”) would look like this:  This format allows us to compare tuples for equality by directly comparing their raw bytes. This also enables fast hashing of tuples. ### Hash map for performing aggregations This patch introduces `UnsafeFixedWidthAggregationMap`, a hash map for performing aggregations where the aggregation result columns are fixed-with. This map's keys and values are `Row` objects. `UnsafeFixedWidthAggregationMap` is implemented on top of `BytesToBytesMap`, an append-only map which supports byte-array keys and values. `BytesToBytesMap` stores pointers to key and value tuples. For each record with a new key, we copy the key and create the aggregation value buffer for that key and put them in a buffer. The hash table then simply stores pointers to the key and value. For each record with an existing key, we simply run the aggregation function to update the values in place. This map is implemented using open hashing with triangular sequence probing. Each entry stores two words in a long array: the first word stores the address of the key and the second word stores the relative offset from the key tuple to the value tuple, as well as the key's 32-bit hashcode. By storing the full hashcode, we reduce the number of equality checks that need to be performed to handle position collisions ()since the chance of hashcode collision is much lower than position collision). `UnsafeFixedWidthAggregationMap` allows regular Spark SQL `Row` objects to be used when probing the map. Internally, it encodes these rows into `UnsafeRow` format using `UnsafeRowConverter`. This conversion has a small overhead that can be eliminated in the future once we use UnsafeRows in other operators. <!-- Reviewable:start --> [<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/5725) <!-- Reviewable:end --> Author: Josh Rosen <joshrosen@databricks.com> Closes #5725 from JoshRosen/unsafe and squashes the following commits: eeee512 [Josh Rosen] Add converters for Null, Boolean, Byte, and Short columns. 81f34f8 [Josh Rosen] Follow 'place children last' convention for GeneratedAggregate 1bc36cc [Josh Rosen] Refactor UnsafeRowConverter to avoid unnecessary boxing. 017b2dc [Josh Rosen] Remove BytesToBytesMap.finalize() 50e9671 [Josh Rosen] Throw memory leak warning even in case of error; add warning about code duplication 70a39e4 [Josh Rosen] Split MemoryManager into ExecutorMemoryManager and TaskMemoryManager: 6e4b192 [Josh Rosen] Remove an unused method from ByteArrayMethods. de5e001 [Josh Rosen] Fix debug vs. trace in logging message. a19e066 [Josh Rosen] Rename unsafe Java test suites to match Scala test naming convention. 78a5b84 [Josh Rosen] Add logging to MemoryManager ce3c565 [Josh Rosen] More comments, formatting, and code cleanup. 529e571 [Josh Rosen] Measure timeSpentResizing in nanoseconds instead of milliseconds. 3ca84b2 [Josh Rosen] Only zero the used portion of groupingKeyConversionScratchSpace 162caf7 [Josh Rosen] Fix test compilation b45f070 [Josh Rosen] Don't redundantly store the offset from key to value, since we can compute this from the key size. a8e4a3f [Josh Rosen] Introduce MemoryManager interface; add to SparkEnv. 0925847 [Josh Rosen] Disable MiMa checks for new unsafe module cde4132 [Josh Rosen] Add missing pom.xml 9c19fc0 [Josh Rosen] Add configuration options for heap vs. offheap 6ffdaa1 [Josh Rosen] Null handling improvements in UnsafeRow. 31eaabc [Josh Rosen] Lots of TODO and doc cleanup. a95291e [Josh Rosen] Cleanups to string handling code afe8dca [Josh Rosen] Some Javadoc cleanup f3dcbfe [Josh Rosen] More mod replacement 854201a [Josh Rosen] Import and comment cleanup 06e929d [Josh Rosen] More warning cleanup ef6b3d3 [Josh Rosen] Fix a bunch of FindBugs and IntelliJ inspections 29a7575 [Josh Rosen] Remove debug logging 49aed30 [Josh Rosen] More long -> int conversion. b26f1d3 [Josh Rosen] Fix bug in murmur hash implementation. 765243d [Josh Rosen] Enable optional performance metrics for hash map. 23a440a [Josh Rosen] Bump up default hash map size 628f936 [Josh Rosen] Use ints intead of longs for indexing. 92d5a06 [Josh Rosen] Address a number of minor code review comments. 1f4b716 [Josh Rosen] Merge Unsafe code into the regular GeneratedAggregate, guarded by a configuration flag; integrate planner support and re-enable all tests. d85eeff [Josh Rosen] Add basic sanity test for UnsafeFixedWidthAggregationMap bade966 [Josh Rosen] Comment update (bumping to refresh GitHub cache...) b3eaccd [Josh Rosen] Extract aggregation map into its own class. d2bb986 [Josh Rosen] Update to implement new Row methods added upstream 58ac393 [Josh Rosen] Use UNSAFE allocator in GeneratedAggregate (TODO: make this configurable) 7df6008 [Josh Rosen] Optimizations related to zeroing out memory: c1b3813 [Josh Rosen] Fix bug in UnsafeMemoryAllocator.free(): 738fa33 [Josh Rosen] Add feature flag to guard UnsafeGeneratedAggregate c55bf66 [Josh Rosen] Free buffer once iterator has been fully consumed. 62ab054 [Josh Rosen] Optimize for fact that get() is only called on String columns. c7f0b56 [Josh Rosen] Reuse UnsafeRow pointer in UnsafeRowConverter ae39694 [Josh Rosen] Add finalizer as "cleanup method of last resort" c754ae1 [Josh Rosen] Now that the store*() contract has been stregthened, we can remove an extra lookup f764d13 [Josh Rosen] Simplify address + length calculation in Location. 079f1bf [Josh Rosen] Some clarification of the BytesToBytesMap.lookup() / set() contract. 1a483c5 [Josh Rosen] First version that passes some aggregation tests: fc4c3a8 [Josh Rosen] Sketch how the converters will be used in UnsafeGeneratedAggregate 53ba9b7 [Josh Rosen] Start prototyping Java Row -> UnsafeRow converters 1ff814d [Josh Rosen] Add reminder to free memory on iterator completion 8a8f9df [Josh Rosen] Add skeleton for GeneratedAggregate integration. 5d55cef [Josh Rosen] Add skeleton for Row implementation. f03e9c1 [Josh Rosen] Play around with Unsafe implementations of more string methods. ab68e08 [Josh Rosen] Begin merging the UTF8String implementations. 480a74a [Josh Rosen] Initial import of code from Databricks unsafe utils repo. |
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| catalyst | ||
| core | ||
| hive | ||
| hive-thriftserver | ||
| README.md | ||
Spark SQL
This module provides support for executing relational queries expressed in either SQL or a LINQ-like Scala DSL.
Spark SQL is broken up into four subprojects:
- Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
- Execution (sql/core) - A query planner / execution engine for translating Catalyst’s logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files.
- Hive Support (sql/hive) - Includes an extension of SQLContext called HiveContext that allows users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allows users to run queries that include Hive UDFs, UDAFs, and UDTFs.
- HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.
Other dependencies for developers
In order to create new hive test cases (i.e. a test suite based on HiveComparisonTest),
you will need to setup your development environment based on the following instructions.
If you are working with Hive 0.12.0, you will need to set several environmental variables as follows.
export HIVE_HOME="<path to>/hive/build/dist"
export HIVE_DEV_HOME="<path to>/hive/"
export HADOOP_HOME="<path to>/hadoop-1.0.4"
If you are working with Hive 0.13.1, the following steps are needed:
- Download Hive's 0.13.1 and set
HIVE_HOMEwithexport HIVE_HOME="<path to hive>". Please do not setHIVE_DEV_HOME(See SPARK-4119). - Set
HADOOP_HOMEwithexport HADOOP_HOME="<path to hadoop>" - Download all Hive 0.13.1a jars (Hive jars actually used by Spark) from here and replace corresponding original 0.13.1 jars in
$HIVE_HOME/lib. - Download Kryo 2.21 jar (Note: 2.22 jar does not work) and Javolution 5.5.1 jar to
$HIVE_HOME/lib. - This step is optional. But, when generating golden answer files, if a Hive query fails and you find that Hive tries to talk to HDFS or you find weird runtime NPEs, set the following in your test suite...
val testTempDir = Utils.createTempDir()
// We have to use kryo to let Hive correctly serialize some plans.
sql("set hive.plan.serialization.format=kryo")
// Explicitly set fs to local fs.
sql(s"set fs.default.name=file://$testTempDir/")
// Ask Hive to run jobs in-process as a single map and reduce task.
sql("set mapred.job.tracker=local")
Using the console
An interactive scala console can be invoked by running build/sbt hive/console.
From here you can execute queries with HiveQl and manipulate DataFrame by using DSL.
catalyst$ build/sbt hive/console
[info] Starting scala interpreter...
import org.apache.spark.sql.catalyst.analysis._
import org.apache.spark.sql.catalyst.dsl._
import org.apache.spark.sql.catalyst.errors._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.rules._
import org.apache.spark.sql.catalyst.util._
import org.apache.spark.sql.execution
import org.apache.spark.sql.functions._
import org.apache.spark.sql.hive._
import org.apache.spark.sql.hive.test.TestHive._
import org.apache.spark.sql.types._
Type in expressions to have them evaluated.
Type :help for more information.
scala> val query = sql("SELECT * FROM (SELECT * FROM src) a")
query: org.apache.spark.sql.DataFrame = org.apache.spark.sql.DataFrame@74448eed
Query results are DataFrames and can be operated as such.
scala> query.collect()
res2: Array[org.apache.spark.sql.Row] = Array([238,val_238], [86,val_86], [311,val_311], [27,val_27]...
You can also build further queries on top of these DataFrames using the query DSL.
scala> query.where(query("key") > 30).select(avg(query("key"))).collect()
res3: Array[org.apache.spark.sql.Row] = Array([274.79025423728814])