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HBASE-15473: Documentation for the usage of hbase dataframe user api (JSON, Avro, etc)

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Weiqing Yang 2016-06-02 16:17:33 -07:00 committed by Misty Stanley-Jones
parent 28802decc8
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src/main/asciidoc/_chapters/spark.adoc
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@ -384,29 +384,157 @@ returns a tuple with the first value being the row key and the second value
being an object of FamiliesQualifiersValues, which will contain all the being an object of FamiliesQualifiersValues, which will contain all the
values for this row for all column families. values for this row for all column families.
== SparkSQL/DataFrames == SparkSQL/DataFrames
http://spark.apache.org/sql/[SparkSQL] is a subproject of Spark that supports HBase-Spark Connector (in HBase-Spark Module) leverages
SQL that will compute down to a Spark DAG. In addition,SparkSQL is a heavy user link:https://databricks.com/blog/2015/01/09/spark-sql-data-sources-api-unified-data-access-for-the-spark-platform.html[DataSource API]
of DataFrames. DataFrames are like RDDs with schema information. (link:https://issues.apache.org/jira/browse/SPARK-3247[SPARK-3247])
introduced in Spark-1.2.0, bridges the gap between simple HBase KV store and complex
relational SQL queries and enables users to perform complex data analytical work
on top of HBase using Spark. HBase Dataframe is a standard Spark Dataframe, and is able to
interact with any other data sources such as Hive, Orc, Parquet, JSON, etc.
HBase-Spark Connector applies critical techniques such as partition pruning, column pruning,
predicate pushdown and data locality.
The HBase-Spark module includes support for Spark SQL and DataFrames, which allows To use HBase-Spark connector, users need to define the Catalog for the schema mapping
you to write SparkSQL directly on HBase tables. In addition the HBase-Spark between HBase and Spark tables, prepare the data and populate the HBase table,
will push down query filtering logic to HBase. then load HBase DataFrame. After that, users can do integrated query and access records
in HBase table with SQL query. Following illustrates the basic procedure.
In HBaseSparkConf, four parameters related to timestamp can be set. They are TIMESTAMP, === Define catalog
MIN_TIMESTAMP, MAX_TIMESTAMP and MAX_VERSIONS respectively. Users can query records
with different timestamps or time ranges with MIN_TIMESTAMP and MAX_TIMESTAMP. [source, scala]
In the meantime, use concrete value instead of tsSpecified and oldMs in the examples below. ----
def catalog = s"""{
       |"table":{"namespace":"default", "name":"table1"},
       |"rowkey":"key",
       |"columns":{
         |"col0":{"cf":"rowkey", "col":"key", "type":"string"},
         |"col1":{"cf":"cf1", "col":"col1", "type":"boolean"},
         |"col2":{"cf":"cf2", "col":"col2", "type":"double"},
         |"col3":{"cf":"cf3", "col":"col3", "type":"float"},
         |"col4":{"cf":"cf4", "col":"col4", "type":"int"},
         |"col5":{"cf":"cf5", "col":"col5", "type":"bigint"},
         |"col6":{"cf":"cf6", "col":"col6", "type":"smallint"},
         |"col7":{"cf":"cf7", "col":"col7", "type":"string"},
         |"col8":{"cf":"cf8", "col":"col8", "type":"tinyint"}
       |}
     |}""".stripMargin
----
Catalog defines a mapping between HBase and Spark tables. There are two critical parts of this catalog.
One is the rowkey definition and the other is the mapping between table column in Spark and
the column family and column qualifier in HBase. The above defines a schema for a HBase table
with name as table1, row key as key and a number of columns (col1 `-` col8). Note that the rowkey
also has to be defined in details as a column (col0), which has a specific cf (rowkey).
=== Save the DataFrame
[source, scala]
----
case class HBaseRecord(
col0: String,
col1: Boolean,
col2: Double,
col3: Float,
col4: Int,       
col5: Long,
col6: Short,
col7: String,
col8: Byte)
object HBaseRecord
{                                                                                                             
def apply(i: Int, t: String): HBaseRecord = {
val s = s"""row${"%03d".format(i)}"""       
HBaseRecord(s,
i % 2 == 0,
i.toDouble,
i.toFloat,  
i,
i.toLong,
i.toShort,  
s"String$i: $t",      
i.toByte)
}
}
val data = (0 to 255).map { i =>  HBaseRecord(i, "extra")}
sc.parallelize(data).toDF.write.options(
 Map(HBaseTableCatalog.tableCatalog -> catalog, HBaseTableCatalog.newTable -> "5"))
 .format("org.apache.hadoop.hbase.spark ")
 .save()
----
`data` prepared by the user is a local Scala collection which has 256 HBaseRecord objects.
`sc.parallelize(data)` function distributes `data` to form an RDD. `toDF` returns a DataFrame.
`write` function returns a DataFrameWriter used to write the DataFrame to external storage
systems (e.g. HBase here). Given a DataFrame with specified schema `catalog`, `save` function
will create an HBase table with 5 regions and save the DataFrame inside.
=== Load the DataFrame
[source, scala]
----
def withCatalog(cat: String): DataFrame = {
sqlContext
.read
.options(Map(HBaseTableCatalog.tableCatalog->cat))
.format("org.apache.hadoop.hbase.spark")
.load()
}
val df = withCatalog(catalog)
----
In ‘withCatalog’ function, sqlContext is a variable of SQLContext, which is the entry point
for working with structured data (rows and columns) in Spark.
`read` returns a DataFrameReader that can be used to read data in as a DataFrame.
`option` function adds input options for the underlying data source to the DataFrameReader,
and `format` function specifies the input data source format for the DataFrameReader.
The `load()` function loads input in as a DataFrame. The date frame `df` returned
by `withCatalog` function could be used to access HBase table, such as 4.4 and 4.5.
=== Language Integrated Query
[source, scala]
----
val s = df.filter(($"col0" <= "row050" && $"col0" > "row040") ||
$"col0" === "row005" ||
$"col0" <= "row005")
.select("col0", "col1", "col4")
s.show
----
DataFrame can do various operations, such as join, sort, select, filter, orderBy and so on.
`df.filter` above filters rows using the given SQL expression. `select` selects a set of columns:
`col0`, `col1` and `col4`.
=== SQL Query
[source, scala]
----
df.registerTempTable("table1")
sqlContext.sql("select count(col1) from table1").show
----
`registerTempTable` registers `df` DataFrame as a temporary table using the table name `table1`.
The lifetime of this temporary table is tied to the SQLContext that was used to create `df`.
`sqlContext.sql` function allows the user to execute SQL queries.
=== Others
.Query with different timestamps .Query with different timestamps
==== ====
In HBaseSparkConf, four parameters related to timestamp can be set. They are TIMESTAMP,
MIN_TIMESTAMP, MAX_TIMESTAMP and MAX_VERSIONS respectively. Users can query records with
different timestamps or time ranges with MIN_TIMESTAMP and MAX_TIMESTAMP. In the meantime,
use concrete value instead of tsSpecified and oldMs in the examples below.
The example below shows how to load df DataFrame with different timestamps. The example below shows how to load df DataFrame with different timestamps.
tsSpecified is specified by the user. tsSpecified is specified by the user.
HBaseTableCatalog defines the HBase and Relation relation schema. HBaseTableCatalog defines the HBase and Relation relation schema.
writeCatalog defines catalog for the schema mapping. writeCatalog defines catalog for the schema mapping.
[source, scala]
---- ----
val df = sqlContext.read val df = sqlContext.read
.options(Map(HBaseTableCatalog.tableCatalog -> writeCatalog, HBaseSparkConf.TIMESTAMP -> tsSpecified.toString)) .options(Map(HBaseTableCatalog.tableCatalog -> writeCatalog, HBaseSparkConf.TIMESTAMP -> tsSpecified.toString))
@ -416,6 +544,8 @@ val df = sqlContext.read
The example below shows how to load df DataFrame with different time ranges. The example below shows how to load df DataFrame with different time ranges.
oldMs is specified by the user. oldMs is specified by the user.
[source, scala]
---- ----
val df = sqlContext.read val df = sqlContext.read
.options(Map(HBaseTableCatalog.tableCatalog -> writeCatalog, HBaseSparkConf.MIN_TIMESTAMP -> "0", .options(Map(HBaseTableCatalog.tableCatalog -> writeCatalog, HBaseSparkConf.MIN_TIMESTAMP -> "0",
@ -423,132 +553,138 @@ val df = sqlContext.read
.format("org.apache.hadoop.hbase.spark") .format("org.apache.hadoop.hbase.spark")
.load() .load()
---- ----
After loading df DataFrame, users can query data. After loading df DataFrame, users can query data.
----
df.registerTempTable("table")
sqlContext.sql("select count(col1) from table").show
----
====
=== Predicate Push Down
There are two examples of predicate push down in the HBase-Spark implementation.
The first example shows the push down of filtering logic on the RowKey. HBase-Spark
will reduce the filters on RowKeys down to a set of Get and/or Scan commands.
NOTE: The Scans are distributed scans, rather than a single client scan operation.
If the query looks something like the following, the logic will push down and get
the rows through 3 Gets and 0 Scans. We can do gets because all the operations
are `equal` operations.
[source,sql]
----
SELECT
KEY_FIELD,
B_FIELD,
A_FIELD
FROM hbaseTmp
WHERE (KEY_FIELD = 'get1' or KEY_FIELD = 'get2' or KEY_FIELD = 'get3')
----
Now let's look at an example where we will end up doing two scans on HBase.
[source, sql]
----
SELECT
KEY_FIELD,
B_FIELD,
A_FIELD
FROM hbaseTmp
WHERE KEY_FIELD < 'get2' or KEY_FIELD > 'get3'
----
In this example we will get 0 Gets and 2 Scans. One scan will load everything
from the first row in the table until “get2” and the second scan will get
everything from “get3” until the last row in the table.
The next query is a good example of having a good deal of range checks. However
the ranges overlap. To the code will be smart enough to get the following data
in a single scan that encompasses all the data asked by the query.
[source, sql]
----
SELECT
KEY_FIELD,
B_FIELD,
A_FIELD
FROM hbaseTmp
WHERE
(KEY_FIELD >= 'get1' and KEY_FIELD <= 'get3') or
(KEY_FIELD > 'get3' and KEY_FIELD <= 'get5')
----
The second example of push down functionality offered by the HBase-Spark module
is the ability to push down filter logic for column and cell fields. Just like
the RowKey logic, all query logic will be consolidated into the minimum number
of range checks and equal checks by sending a Filter object along with the Scan
with information about consolidated push down predicates
.SparkSQL Code Example
====
This example shows how we can interact with HBase with SQL.
[source, scala] [source, scala]
---- ----
val sc = new SparkContext("local", "test") df.registerTempTable("table")
val config = new HBaseConfiguration() sqlContext.sql("select count(col1) from table").show
new HBaseContext(sc, TEST_UTIL.getConfiguration)
val sqlContext = new SQLContext(sc)
df = sqlContext.load("org.apache.hadoop.hbase.spark",
Map("hbase.columns.mapping" ->
"KEY_FIELD STRING :key, A_FIELD STRING c:a, B_FIELD STRING c:b",
"hbase.table" -> "t1"))
df.registerTempTable("hbaseTmp")
val results = sqlContext.sql("SELECT KEY_FIELD, B_FIELD FROM hbaseTmp " +
"WHERE " +
"(KEY_FIELD = 'get1' and B_FIELD < '3') or " +
"(KEY_FIELD >= 'get3' and B_FIELD = '8')").take(5)
---- ----
====
There are three major parts of this example that deserve explaining. .Native Avro support
====
HBase-Spark Connector support different data formats like Avro, Jason, etc. The use case below
shows how spark supports Avro. User can persist the Avro record into HBase directly. Internally,
the Avro schema is converted to a native Spark Catalyst data type automatically.
Note that both key-value parts in an HBase table can be defined in Avro format.
The sqlContext.load function:: 1) Define catalog for the schema mapping:
In the sqlContext.load function we see two
parameters. The first of these parameters is pointing Spark to the HBase
DefaultSource class that will act as the interface between SparkSQL and HBase.
A map of key value pairs::
In this example we have two keys in our map, `hbase.columns.mapping` and
`hbase.table`. The `hbase.table` directs SparkSQL to use the given HBase table.
The `hbase.columns.mapping` key give us the logic to translate HBase columns to
SparkSQL columns.
+
The `hbase.columns.mapping` is a string that follows the following format
+
[source, scala] [source, scala]
---- ----
(SparkSQL.ColumnName) (SparkSQL.ColumnType) (HBase.ColumnFamily):(HBase.Qualifier) def catalog = s"""{
---- |"table":{"namespace":"default", "name":"Avrotable"},
+ |"rowkey":"key",
In the example below we see the definition of three fields. Because KEY_FIELD has |"columns":{
no ColumnFamily, it is the RowKey. |"col0":{"cf":"rowkey", "col":"key", "type":"string"},
+ |"col1":{"cf":"cf1", "col":"col1", "type":"binary"}
---- |}
KEY_FIELD STRING :key, A_FIELD STRING c:a, B_FIELD STRING c:b |}""".stripMargin
---- ----
The registerTempTable function:: `catalog` is a schema for a HBase table named `Avrotable`. row key as key and
This is a SparkSQL function that allows us now to be free of Scala when accessing one column col1. The rowkey also has to be defined in details as a column (col0),
our HBase table directly with SQL with the table name of "hbaseTmp". which has a specific cf (rowkey).
The last major point to note in the example is the `sqlContext.sql` function, which 2) Prepare the Data:
allows the user to ask their questions in SQL which will be pushed down to the
DefaultSource code in the HBase-Spark module. The result of this command will be [source, scala]
a DataFrame with the Schema of KEY_FIELD and B_FIELD. ----
object AvroHBaseRecord {
val schemaString =
s"""{"namespace": "example.avro",
| "type": "record", "name": "User",
| "fields": [
| {"name": "name", "type": "string"},
| {"name": "favorite_number", "type": ["int", "null"]},
| {"name": "favorite_color", "type": ["string", "null"]},
| {"name": "favorite_array", "type": {"type": "array", "items": "string"}},
| {"name": "favorite_map", "type": {"type": "map", "values": "int"}}
| ] }""".stripMargin
val avroSchema: Schema = {
val p = new Schema.Parser
p.parse(schemaString)
}
def apply(i: Int): AvroHBaseRecord = {
val user = new GenericData.Record(avroSchema);
user.put("name", s"name${"%03d".format(i)}")
user.put("favorite_number", i)
user.put("favorite_color", s"color${"%03d".format(i)}")
val favoriteArray = new GenericData.Array[String](2, avroSchema.getField("favorite_array").schema())
favoriteArray.add(s"number${i}")
favoriteArray.add(s"number${i+1}")
user.put("favorite_array", favoriteArray)
import collection.JavaConverters._
val favoriteMap = Map[String, Int](("key1" -> i), ("key2" -> (i+1))).asJava
user.put("favorite_map", favoriteMap)
val avroByte = AvroSedes.serialize(user, avroSchema)
AvroHBaseRecord(s"name${"%03d".format(i)}", avroByte)
}
}
val data = (0 to 255).map { i =>
AvroHBaseRecord(i)
}
----
`schemaString` is defined first, then it is parsed to get `avroSchema`. `avroSchema` is used to
generate `AvroHBaseRecord`. `data` prepared by users is a local Scala collection
which has 256 `AvroHBaseRecord` objects.
3) Save DataFrame:
[source, scala]
----
sc.parallelize(data).toDF.write.options(
Map(HBaseTableCatalog.tableCatalog -> catalog, HBaseTableCatalog.newTable -> "5"))
.format("org.apache.spark.sql.execution.datasources.hbase")
.save()
----
Given a data frame with specified schema `catalog`, above will create an HBase table with 5
regions and save the data frame inside.
4) Load the DataFrame
[source, scala]
----
def avroCatalog = s"""{
|"table":{"namespace":"default", "name":"avrotable"},
|"rowkey":"key",
|"columns":{
|"col0":{"cf":"rowkey", "col":"key", "type":"string"},
|"col1":{"cf":"cf1", "col":"col1", "avro":"avroSchema"}
|}
|}""".stripMargin
def withCatalog(cat: String): DataFrame = {
sqlContext
.read
.options(Map("avroSchema" -> AvroHBaseRecord.schemaString, HBaseTableCatalog.tableCatalog -> avroCatalog))
.format("org.apache.spark.sql.execution.datasources.hbase")
.load()
}
val df = withCatalog(catalog)
----
In `withCatalog` function, `read` returns a DataFrameReader that can be used to read data in as a DataFrame.
The `option` function adds input options for the underlying data source to the DataFrameReader.
There are two options: one is to set `avroSchema` as `AvroHBaseRecord.schemaString`, and one is to
set `HBaseTableCatalog.tableCatalog` as `avroCatalog`. The `load()` function loads input in as a DataFrame.
The date frame `df` returned by `withCatalog` function could be used to access the HBase table.
5) SQL Query
[source, scala]
----
df.registerTempTable("avrotable")
val c = sqlContext.sql("select count(1) from avrotable").
----
After loading df DataFrame, users can query data. registerTempTable registers df DataFrame
as a temporary table using the table name avrotable. `sqlContext.sql` function allows the
user to execute SQL queries.
==== ====