Hyperspace初体验：Delta Lake表索引
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Hyperspace初体验：Delta Lake表索引
1.简介
Hyperspace是一个由微软开发的开源的数据湖索引子系统。
1.1 特性
-
提供了一套定义完好的索引管理API （4.1 建索引, 5.2 增量刷新）
- 为用户提供更大的自由度，毕竟用户是最了解自己用例的人
- 不尝试去解决所有问题，有些问题没有固定答案
-
独立于数据和元数据，索引有自己的元数据/日志（4.2 Hyperspace日志）
-
能感知到底层数据的版本，同数据一起“时间旅行” （5.1 Time Travel支持）
-
因为Hybrid Scan，索引与原始数据不需要完全同步 （5.3 Hybrid Scan）
1.2 开发中
Hyperspace项目目前比较活跃，还有很多重要功能在开发中，想要预览的话可以尝试用 master 分支代码:
-
Spark 3.x: https://github.com/microsoft/hyperspace/issues/407
-
Iceberg: https://github.com/microsoft/hyperspace/issues/318
-
索引实现
- Z-ordering: https://github.com/microsoft/hyperspace/issues/515
- Data skipping: https://github.com/microsoft/hyperspace/issues/441
2.安装
2.1 软件版本
- Hyperspace 0.4.0支持Delta Lake表的索引
- Delta Lake 0.6.1是最后一个支持Spark 2.x的版本
- Spark 2.4.2和Scala 2.12.8与上述Hyperspace, Delta Lake版本兼容
因为对 Apache Spark 3.x的支持还在开发中, Spark 2.4.2看起来是目前唯一能让Hyperspace和Delta Lake共同工作的Spark版本。
2.2 CLI启动
bin/spark-shell --packages io.delta:delta-core_2.12:0.6.1,com.microsoft.hyperspace:hyperspace-core_2.12:0.4.0 --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog"
Ivy Default Cache set to: /Users/daichen/.ivy2/cache
The jars for the packages stored in: /Users/daichen/.ivy2/jars
:: loading settings :: url = jar:file:/Users/daichen/Software/spark-2.4.2-bin-hadoop2.7/jars/ivy-2.4.0.jar!/org/apache/ivy/core/settings/ivysettings.xml
io.delta#delta-core_2.12 added as a dependency
com.microsoft.hyperspace#hyperspace-core_2.11 added as a dependency
:: resolving dependencies :: org.apache.spark#spark-submit-parent-040a057e-2b7f-4d3a-a658-b93e6474dc47;1.0
confs: [default]
found io.delta#delta-core_2.12;0.6.1 in central
found org.antlr#antlr4;4.7 in central
found org.antlr#antlr4-runtime;4.7 in local-m2-cache
found org.antlr#antlr-runtime;3.5.2 in local-m2-cache
found org.antlr#ST4;4.0.8 in local-m2-cache
found org.abego.treelayout#org.abego.treelayout.core;1.0.3 in local-m2-cache
found org.glassfish#javax.json;1.0.4 in local-m2-cache
found com.ibm.icu#icu4j;58.2 in local-m2-cache
found com.microsoft.hyperspace#hyperspace-core_2.11;0.3.0 in central
:: resolution report :: resolve 351ms :: artifacts dl 9ms
:: modules in use:
com.ibm.icu#icu4j;58.2 from local-m2-cache in [default]
com.microsoft.hyperspace#hyperspace-core_2.11;0.3.0 from central in [default]
io.delta#delta-core_2.12;0.6.1 from central in [default]
org.abego.treelayout#org.abego.treelayout.core;1.0.3 from local-m2-cache in [default]
org.antlr#ST4;4.0.8 from local-m2-cache in [default]
org.antlr#antlr-runtime;3.5.2 from local-m2-cache in [default]
org.antlr#antlr4;4.7 from central in [default]
org.antlr#antlr4-runtime;4.7 from local-m2-cache in [default]
org.glassfish#javax.json;1.0.4 from local-m2-cache in [default]
---------------------------------------------------------------------
| | modules || artifacts |
| conf | number| search|dwnlded|evicted|| number|dwnlded|
---------------------------------------------------------------------
| default | 9 | 0 | 0 | 0 || 9 | 0 |
---------------------------------------------------------------------
:: retrieving :: org.apache.spark#spark-submit-parent-040a057e-2b7f-4d3a-a658-b93e6474dc47
confs: [default]
0 artifacts copied, 9 already retrieved (0kB/8ms)
21/12/09 16:15:53 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context Web UI available at http://192.168.4.51:4040
Spark context available as 'sc' (master = local[*], app id = local-1639095359127).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\\ \\/ _ \\/ _ `/ __/ '_/
/___/ .__/\\_,_/_/ /_/\\_\\ version 2.4.2
/_/
Using Scala version 2.12.8 (OpenJDK 64-Bit Server VM, Java 1.8.0_312)
Type in expressions to have them evaluated.
Type :help for more information.
scala>
3.Delta Lake表
3.1 建表
本文所有例子都使用Spark自带的 employees.json 数据集。首先，我们就先用这个数据集建一张Delta Lake的分区表。
scala> val employees = spark.read.json("examples/src/main/resources/employees.json")
employees: org.apache.spark.sql.DataFrame = [name: string, salary: bigint]
scala> employees.show()
+-------+------+
| name|salary|
+-------+------+
|Michael| 3000|
| Andy| 4500|
| Justin| 3500|
| Berta| 4000|
+-------+------+
scala> employees.write.partitionBy("salary").format("delta").save("/tmp/delta-table/employees")
现在可以看一下Delta Lake的文件夹结构:
$ tree /tmp/delta-table/employees
/tmp/delta-table/employees
├── _delta_log
│ └── 00000000000000000000.json
├── salary=3000
│ └── part-00000-e0493ade-23d4-402e-bf43-0ad727b0754e.c000.snappy.parquet
├── salary=3500
│ └── part-00000-5bae7fa3-43c3-43a7-85a2-286848b5d589.c000.snappy.parquet
├── salary=4000
│ └── part-00000-090c7267-5a47-439a-8ca6-3ae670969ac3.c000.snappy.parquet
└── salary=4500
└── part-00000-375f485f-d021-4625-b598-1f86b3ad8953.c000.snappy.parquet
3.2 Delta Lake日志
Delta日志记录了数据文件中所有的增加和删除操作（因为Data Lake存储，比如S3，不支持文件的更新甚至追加），这个日志本身也是 Parquet格式的:
$ cat /tmp/delta-table/employees/_delta_log/00000000000000000000.json
"commitInfo":"timestamp":1639095874103,"operation":"WRITE","operationParameters":"mode":"ErrorIfExists","partitionBy":"[\\"salary\\"]","isBlindAppend":true,"operationMetrics":"numFiles":"4","numOutputBytes":"1679","numOutputRows":"4"
"protocol":"minReaderVersion":1,"minWriterVersion":2
"metaData":"id":"0c5bbb47-1ee3-4637-883c-61284a73501b","format":"provider":"parquet","options":,"schemaString":"\\"type\\":\\"struct\\",\\"fields\\":[\\"name\\":\\"name\\",\\"type\\":\\"string\\",\\"nullable\\":true,\\"metadata\\":,\\"name\\":\\"salary\\",\\"type\\":\\"long\\",\\"nullable\\":true,\\"metadata\\":]","partitionColumns":["salary"],"configuration":,"createdTime":1639095873565
"add":"path":"salary=3000/part-00000-e0493ade-23d4-402e-bf43-0ad727b0754e.c000.snappy.parquet","partitionValues":"salary":"3000","size":434,"modificationTime":1639095873996,"dataChange":true
"add":"path":"salary=3500/part-00000-5bae7fa3-43c3-43a7-85a2-286848b5d589.c000.snappy.parquet","partitionValues":"salary":"3500","size":425,"modificationTime":1639095874020,"dataChange":true
"add":"path":"salary=4000/part-00000-090c7267-5a47-439a-8ca6-3ae670969ac3.c000.snappy.parquet","partitionValues":"salary":"4000","size":416,"modificationTime":1639095874044,"dataChange":true
"add":"path":"salary=4500/part-00000-375f485f-d021-4625-b598-1f86b3ad8953.c000.snappy.parquet","partitionValues":"salary":"4500","size":404,"modificationTime":1639095874069,"dataChange":true
4.Hyperspace基础
4.1 创建索引
让我们先来创建一个索引（Hyperspace目前主要支持Covering索引，即索引本身也包括其他列数据，这样查询可以直接在索引里完成，无需访问原始数据）:
scala> val employees = spark.read.format("delta").load("/tmp/delta-table/employees")
employees: org.apache.spark.sql.DataFrame = [name: string, salary: bigint]
scala> spark.conf.set("spark.hyperspace.index.sources.fileBasedBuilders",
| "com.microsoft.hyperspace.index.sources.delta.DeltaLakeFileBasedSourceBuilder," +
| "com.microsoft.hyperspace.index.sources.default.DefaultFileBasedSourceBuilder")
scala> import com.microsoft.hyperspace._
import com.microsoft.hyperspace._
scala> val hyperspace = new Hyperspace(spark)
hyperspace: com.microsoft.hyperspace.Hyperspace = com.microsoft.hyperspace.Hyperspace@515a3572
scala> import com.microsoft.hyperspace.index._
import com.microsoft.hyperspace.index._
scala> hyperspace.createIndex(employees, IndexConfig("deltaIndex", indexedColumns = Seq("name"), includedColumns = Seq("salary")))
4.2 Hyperspace日志
$ spark-2.4.2-bin-hadoop2.7 $ tree spark-warehouse
spark-warehouse
└── indexes
└── deltaIndex
├── _hyperspace_log
│ ├── 0
│ ├── 1
│ └── latestStable
└── v__=0
├── _SUCCESS
├── part-00071-46fed65d-a011-4d69-9267-30c40c623a78_00071.c000.snappy.parquet
├── part-00164-46fed65d-a011-4d69-9267-30c40c623a78_00164.c000.snappy.parquet
├── part-00165-46fed65d-a011-4d69-9267-30c40c623a78_00165.c000.snappy.parquet
└── part-00169-46fed65d-a011-4d69-9267-30c40c623a78_00169.c000.snappy.parquet
4.3 索引的Explain
scala> val query = employees.filter(employees("name") === "Andy").select("salary")
query: org.apache.spark.sql.DataFrame = [salary: bigint]
scala> query.explain
== Physical Plan ==
*(1) Project [salary#325L]
+- *(1) Filter (isnotnull(name#324) && (name#324 = Andy))
+- *(1) FileScan parquet [name#324,salary#325L] Batched: true, Format: Parquet, Location: TahoeLogFileIndex[file:/tmp/delta-table/employees], PartitionCount: 4, PartitionFilters: [], PushedFilters: [IsNotNull(name), EqualTo(name,Andy)], ReadSchema: struct<name:string>
scala> query.show
+------+
|salary|
+------+
| 4500|
+------+
scala> spark.enableHyperspace
res3: org.apache.spark.sql.SparkSession = org.apache.spark.sql.SparkSession@ac52e35
scala> query.explain
== Physical Plan ==
*(1) Project [salary#325L]
+- *(1) Filter (isnotnull(name#324) && (name#324 = Andy))
+- *(1) FileScan Hyperspace(Type: CI, Name: deltaIndex, LogVersion: 1) [name#324,salary#325L] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/Users/daichen/Software/spark-2.4.2-bin-hadoop2.7/spark-warehouse/indexes/..., PartitionFilters: [], PushedFilters: [IsNotNull(name), EqualTo(name,Andy)], ReadSchema: struct<name:string,salary:bigint>
scala> query.show
+------+
|salary|
+------+
| 4500|
+------+
除了Spark DataFrame的Explain，Hyperspace自己的Explain提供了更多细节:
scala> hyperspace.explain(query, verbose = true)
=============================================================
Plan with indexes:
=============================================================
Project [salary#325L]
+- Filter (isnotnull(name#324) && (name#324 = Andy))
<----+- FileScan Hyperspace(Type: CI, Name: deltaIndex, LogVersion: 1) [name#324,salary#325L] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/Users/daichen/Software/spark-2.4.2-bin-hadoop2.7/spark-warehouse/indexes/..., PartitionFilters: [], PushedFilters: [IsNotNull(name), EqualTo(name,Andy)], ReadSchema: struct<name:string,salary:bigint>---->
=============================================================
Plan without indexes:
=============================================================
Project [salary#325L]
+- Filter (isnotnull(name#324) && (name#324 = Andy))
<----+- FileScan parquet [name#324,salary#325L] Batched: true, Format: Parquet, Location: TahoeLogFileIndex[file:/tmp/delta-table/employees], PartitionCount: 4, PartitionFilters: [], PushedFilters: [IsNotNull(name), EqualTo(name,Andy)], ReadSchema: struct<name:string>---->
=============================================================
Indexes used:
=============================================================
deltaIndex:file:/Users/daichen/Software/spark-2.4.2-bin-hadoop2.7/spark-warehouse/indexes/deltaIndex/v__=0
=============================================================
Physical operator stats:
=============================================================
+-----------------------------------------------------------+-------------------+------------------+----------+
| Physical Operator|Hyperspace Disabled|Hyperspace Enabled|Difference|
+-----------------------------------------------------------+-------------------+------------------+----------+
|*Scan Hyperspace(Type: CI, Name: deltaIndex, LogVersion: 1)| 0| 1| 1|
| *Scan parquet| 1| 0| -1|
| Filter| 1| 1Hyperspace初体验：Delta Lake表索引