Apache Hive is a distributed data warehousing infrastructure. Hive facilitates managing large data sets supporting multiple data formats, including comma-separated value (.csv) TextFile, RCFile, ORC, and Parquet.

The PXF Hive connector reads data stored in a Hive table. This section describes how to use the PXF Hive connector.

Note: When accessing Hive 3, the PXF Hive connector supports using the hive[:*] profiles described below to access Hive 3 external tables only. The Connector does not support using the hive[:*] profiles to access Hive 3 managed (CRUD and insert-only transactional, and temporary) tables. Use the PXF JDBC Connector to access Hive 3 managed tables instead.

Prerequisites

Before working with Hive table data using PXF, ensure that you have met the PXF Hadoop Prerequisites.

If you plan to use PXF filter pushdown with Hive integral types, ensure that the configuration parameter hive.metastore.integral.jdo.pushdown exists and is set to true in the hive-site.xml file in both your Hadoop cluster and $PXF_BASE/servers/default/hive-site.xml. Refer to About Updating Hadoop Configuration for more information.

Hive Data Formats

The PXF Hive connector supports several data formats, and has defined the following profiles for accessing these formats:

File Format Description Profile
TextFile Flat file with data in comma-, tab-, or space-separated value format or JSON notation. hive, hive:text
SequenceFile Flat file consisting of binary key/value pairs. hive
RCFile Record columnar data consisting of binary key/value pairs; high row compression rate. hive, hive:rc
ORC Optimized row columnar data with stripe, footer, and postscript sections; reduces data size. hive, hive:orc
Parquet Compressed columnar data representation. hive
Avro Serialization system with a binary data format. hive

Note: The hive profile supports all file storage formats. It will use the optimal hive[:*] profile for the underlying file format type.

Data Type Mapping

The PXF Hive connector supports primitive and complex data types.

Primitive Data Types

To represent Hive data in Greenplum Database, map data values that use a primitive data type to Greenplum Database columns of the same type.

The following table summarizes external mapping rules for Hive primitive types.

Hive Data Type Greenplum Data Type
boolean bool
int int4
smallint int2
tinyint int2
bigint int8
float float4
double float8
string text
binary bytea
timestamp timestamp

Note: The hive:orc profile does not support the timestamp data type when you specify vectorized query execution (VECTORIZE=true).

Complex Data Types

Hive supports complex data types including array, struct, map, and union. PXF maps each of these complex types to text. You can create Greenplum Database functions or application code to extract subcomponents of these complex data types.

Examples using complex data types with the hive and hive:orc profiles are provided later in this topic.

Note: The hive:orc profile does not support complex types when you specify vectorized query execution (VECTORIZE=true).

Sample Data Set

Examples presented in this topic operate on a common data set. This simple data set models a retail sales operation and includes fields with the following names and data types:

Column Name Data Type
location text
month text
number_of_orders integer
total_sales double

Prepare the sample data set for use:

  1. First, create a text file:

    $ vi /tmp/pxf_hive_datafile.txt

  2. Add the following data to pxf_hive_datafile.txt; notice the use of the comma (,) to separate the four field values:

    Prague,Jan,101,4875.33
Rome,Mar,87,1557.39
Bangalore,May,317,8936.99
Beijing,Jul,411,11600.67
San Francisco,Sept,156,6846.34
Paris,Nov,159,7134.56
San Francisco,Jan,113,5397.89
Prague,Dec,333,9894.77
Bangalore,Jul,271,8320.55
Beijing,Dec,100,4248.41

Make note of the path to pxf_hive_datafile.txt; you will use it in later exercises.

Hive Command Line

The Hive command line is a subsystem similar to that of psql. To start the Hive command line:

$ HADOOP_USER_NAME=hdfs hive

The default Hive database is named default.

Example: Creating a Hive Table

Create a Hive table to expose the sample data set.

  1. Create a Hive table named sales_info in the default database:

    hive> CREATE TABLE sales_info (location string, month string,
        number_of_orders int, total_sales double)
        ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
        STORED AS textfile;

    Notice that:

    • The STORED AS textfile subclause instructs Hive to create the table in Textfile (the default) format. Hive Textfile format supports comma-, tab-, and space-separated values, as well as data specified in JSON notation.
    • The DELIMITED FIELDS TERMINATED BY subclause identifies the field delimiter within a data record (line). The sales_info table field delimiter is a comma (,).

  2. Load the pxf_hive_datafile.txt sample data file into the sales_info table that you just created:

    hive> LOAD DATA LOCAL INPATH '/tmp/pxf_hive_datafile.txt'
        INTO TABLE sales_info;

    In examples later in this section, you will access the sales_info Hive table directly via PXF. You will also insert sales_info data into tables of other Hive file format types, and use PXF to access those directly as well.

  3. Perform a query on sales_info to verify that you loaded the data successfully:

    hive> SELECT * FROM sales_info;

Determining the HDFS Location of a Hive Table

Should you need to identify the HDFS file location of a Hive managed table, reference it using its HDFS file path. You can determine a Hive table’s location in HDFS using the DESCRIBE command. For example:

hive> DESCRIBE EXTENDED sales_info;
Detailed Table Information
...
location:hdfs://<namenode>:<port>/apps/hive/warehouse/sales_info
...

Querying External Hive Data

You can create a Greenplum Database external table to access Hive table data. As described previously, the PXF Hive connector defines specific profiles to support different file formats. These profiles are named hive, hive:text, hive:rc, and hive:orc.

The hive:text and hive:rc profiles are specifically optimized for text and RCFile formats, respectively. The hive:orc profile is optimized for ORC file formats. The hive profile is optimized for all file storage types; you can use the hive profile when the underlying Hive table is composed of multiple partitions with differing file formats.

PXF uses column projection to increase query performance when you access a Hive table using the hive, hive:rc, or hive:orc profiles.

Use the following syntax to create a Greenplum Database external table that references a Hive table:

CREATE EXTERNAL TABLE <table_name>
    ( <column_name> <data_type> [, ...] | LIKE <other_table> )
LOCATION ('pxf://<hive-db-name>.<hive-table-name>
    ?PROFILE=<profile_name>[&SERVER=<server_name>][&PPD=<boolean>][&VECTORIZE=<boolean>]')
FORMAT 'CUSTOM|TEXT' (FORMATTER='pxfwritable_import' | delimiter='<delim>')

Hive connector-specific keywords and values used in the Greenplum Database CREATE EXTERNAL TABLE call are described below.

Keyword Value
<hive‑db‑name> The name of the Hive database. If omitted, defaults to the Hive database named default.
<hive‑table‑name> The name of the Hive table.
PROFILE=<profile_name> <profile_name> must specify one of the values hive, hive:text, hive:rc, or hive:orc.
SERVER=<server_name> The named server configuration that PXF uses to access the data. PXF uses the default server if not specified.
PPD=<boolean> Activate or deactivate predicate pushdown for all queries on this table; this option applies only to the hive, hive:orc, and hive:rc profiles, and overrides a pxf.ppd.hive property setting in the <server_name> configuration.
VECTORIZE=<boolean> When PROFILE=hive:orc, a Boolean value that specifies whether or not PXF uses vectorized query execution when accessing the underlying ORC files. The default value is false, does not use vectorized query execution.
FORMAT (hive and hive:orc profiles) The FORMAT clause must specify 'CUSTOM'. The CUSTOM format requires the built-in pxfwritable_import formatter.
FORMAT (hive:text and hive:rc profiles) The FORMAT clause must specify TEXT. Specify the single ascii character field delimiter in the delimiter='<delim>' formatting option.
Note: Because Hive tables can be backed by one or more files and each file can have a unique layout or schema, PXF requires that the column names that you specify when you create the external table match the column names defined for the Hive table. This allows you to:
  • Create the PXF external table with columns in a different order than the Hive table.
  • Create a PXF external table that reads a subset of the columns in the Hive table.
  • Read a Hive table where the files backing the table have a different number of columns.

Accessing TextFile-Format Hive Tables

You can use the hive and hive:text profiles to access Hive table data stored in TextFile format.

Example: Using the hive Profile

Use the hive profile to create a readable Greenplum Database external table that references the Hive sales_info textfile format table that you created earlier.

  1. Create the external table:

    postgres=# CREATE EXTERNAL TABLE salesinfo_hiveprofile(location text, month text, number_of_orders int, total_sales float8)
            LOCATION ('pxf://default.sales_info?PROFILE=hive')
          FORMAT 'custom' (FORMATTER='pxfwritable_import');

  2. Query the table:

    postgres=# SELECT * FROM salesinfo_hiveprofile;

       location    | month | number_of_orders | total_sales
---------------+-------+------------------+-------------
 Prague        | Jan   |              101 |     4875.33
 Rome          | Mar   |               87 |     1557.39
 Bangalore     | May   |              317 |     8936.99
 ...

Example: Using the hive:text Profile

Use the PXF hive:text profile to create a readable Greenplum Database external table from the Hive sales_info textfile format table that you created earlier.

  1. Create the external table:

    postgres=# CREATE EXTERNAL TABLE salesinfo_hivetextprofile(location text, month text, number_of_orders int, total_sales float8)
             LOCATION ('pxf://default.sales_info?PROFILE=hive:text')
           FORMAT 'TEXT' (delimiter=E',');

    Notice that the FORMAT subclause delimiter value is specified as the single ascii comma character ','. E escapes the character.

  2. Query the external table:

    postgres=# SELECT * FROM salesinfo_hivetextprofile WHERE location='Beijing';

     location | month | number_of_orders | total_sales
----------+-------+------------------+-------------
 Beijing  | Jul   |              411 |    11600.67
 Beijing  | Dec   |              100 |     4248.41
(2 rows)

Accessing RCFile-Format Hive Tables

The RCFile Hive table format is used for row columnar formatted data. The PXF hive:rc profile provides access to RCFile data.

Example: Using the hive:rc Profile

Use the hive:rc profile to query RCFile-formatted data in a Hive table.

  1. Start the hive command line and create a Hive table stored in RCFile format:

    $ HADOOP_USER_NAME=hdfs hive

    hive> CREATE TABLE sales_info_rcfile (location string, month string,
        number_of_orders int, total_sales double)
      ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
      STORED AS rcfile;

  2. Insert the data from the sales_info table into sales_info_rcfile:

    hive> INSERT INTO TABLE sales_info_rcfile SELECT * FROM sales_info;

    A copy of the sample data set is now stored in RCFile format in the Hive sales_info_rcfile table.

  3. Query the sales_info_rcfile Hive table to verify that the data was loaded correctly:

    hive> SELECT * FROM sales_info_rcfile;

  4. Use the PXF hive:rc profile to create a readable Greenplum Database external table that references the Hive sales_info_rcfile table that you created in the previous steps. For example:

    postgres=# CREATE EXTERNAL TABLE salesinfo_hivercprofile(location text, month text, number_of_orders int, total_sales float8)
             LOCATION ('pxf://default.sales_info_rcfile?PROFILE=hive:rc')
           FORMAT 'TEXT' (delimiter=E',');

  5. Query the external table:

    postgres=# SELECT location, total_sales FROM salesinfo_hivercprofile;

       location    | total_sales
---------------+-------------
 Prague        |     4875.33
 Rome          |     1557.39
 Bangalore     |     8936.99
 Beijing       |    11600.67
 ...

Accessing ORC-Format Hive Tables

The Optimized Row Columnar (ORC) file format is a columnar file format that provides a highly efficient way to both store and access HDFS data. ORC format offers improvements over text and RCFile formats in terms of both compression and performance. PXF supports ORC version 1.2.1.

ORC is type-aware and specifically designed for Hadoop workloads. ORC files store both the type of and encoding information for the data in the file. All columns within a single group of row data (also known as stripe) are stored together on disk in ORC format files. The columnar nature of the ORC format type enables read projection, helping avoid accessing unnecessary columns during a query.

ORC also supports predicate pushdown with built-in indexes at the file, stripe, and row levels, moving the filter operation to the data loading phase.

Refer to the Apache orc and the Apache Hive LanguageManual ORC websites for detailed information about the ORC file format.

Profiles Supporting the ORC File Format

When choosing an ORC-supporting profile, consider the following:

  • The hive:orc profile:

    • Reads a single row of data at a time.
    • Supports column projection.
    • Supports complex types. You can access Hive tables composed of array, map, struct, and union data types. PXF serializes each of these complex types to text.

  • The hive:orc profile with VECTORIZE=true:

    • Reads up to 1024 rows of data at once.
    • Supports column projection.
    • Does not support complex types or the timestamp data type.

Example: Using the hive:orc Profile

In the following example, you will create a Hive table stored in ORC format and use the hive:orc profile to query this Hive table.

  1. Create a Hive table with ORC file format:

    $ HADOOP_USER_NAME=hdfs hive

    hive> CREATE TABLE sales_info_ORC (location string, month string,
        number_of_orders int, total_sales double)
      STORED AS ORC;

  2. Insert the data from the sales_info table into sales_info_ORC:

    hive> INSERT INTO TABLE sales_info_ORC SELECT * FROM sales_info;

    A copy of the sample data set is now stored in ORC format in sales_info_ORC.

  3. Perform a Hive query on sales_info_ORC to verify that the data was loaded successfully:

    hive> SELECT * FROM sales_info_ORC;

  4. Start the psql subsystem and turn on timing:

    $ psql -d postgres

    postgres=> \timing
Timing is on.

  5. Use the PXF hive:orc profile to create a Greenplum Database external table that references the Hive table named sales_info_ORC you created in Step 1. The FORMAT clause must specify 'CUSTOM'. The hive:orc CUSTOM format supports only the built-in 'pxfwritable_import' formatter.

    postgres=> CREATE EXTERNAL TABLE salesinfo_hiveORCprofile(location text, month text, number_of_orders int, total_sales float8)
             LOCATION ('pxf://default.sales_info_ORC?PROFILE=hive:orc')
             FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');

  6. Query the external table:

    postgres=> SELECT * FROM salesinfo_hiveORCprofile;

       location    | month | number_of_orders | total_sales 
---------------+-------+------------------+-------------
 Prague        | Jan   |              101 |     4875.33
 Rome          | Mar   |               87 |     1557.39
 Bangalore     | May   |              317 |     8936.99
 ...

Time: 425.416 ms

Example: Using the Vectorized hive:orc Profile

In the following example, you will use the vectorized hive:orc profile to query the sales_info_ORC Hive table that you created in the previous example.

  1. Start the psql subsystem:

    $ psql -d postgres

  2. Use the PXF hive:orc profile to create a readable Greenplum Database external table that references the Hive table named sales_info_ORC that you created in Step 1 of the previous example. The FORMAT clause must specify 'CUSTOM'. The hive:orc CUSTOM format supports only the built-in 'pxfwritable_import' formatter.

    postgres=> CREATE EXTERNAL TABLE salesinfo_hiveVectORC(location text, month text, number_of_orders int, total_sales float8)
             LOCATION ('pxf://default.sales_info_ORC?PROFILE=hive:orc&VECTORIZE=true')
             FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');

  3. Query the external table:

    postgres=> SELECT * FROM salesinfo_hiveVectORC;

       location    | month | number_of_orders | total_sales 
---------------+-------+------------------+-------------
 Prague        | Jan   |              101 |     4875.33
 Rome          | Mar   |               87 |     1557.39
 Bangalore     | May   |              317 |     8936.99
 ...

Time: 425.416 ms

Accessing Parquet-Format Hive Tables

The PXF hive profile supports both non-partitioned and partitioned Hive tables that use the Parquet storage format. Map the table columns using equivalent Greenplum Database data types. For example, if a Hive table is created in the default schema using:

hive> CREATE TABLE hive_parquet_table (location string, month string,
            number_of_orders int, total_sales double)
        STORED AS parquet;

Define the Greenplum Database external table:

postgres=# CREATE EXTERNAL TABLE pxf_parquet_table (location text, month text, number_of_orders int, total_sales double precision)
    LOCATION ('pxf://default.hive_parquet_table?profile=hive')
    FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');

And query the table:

postgres=# SELECT month, number_of_orders FROM pxf_parquet_table;

Accessing Avro-Format Hive Tables

The PXF hive profile supports accessing Hive tables that use the Avro storage format. Map the table columns using equivalent Greenplum Database data types. For example, if a Hive table is created in the default schema using:

hive> CREATE TABLE hive_avro_data_table (id int, name string, user_id string)
	ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.avro.AvroSerDe'
	STORED AS INPUTFORMAT 'org.apache.hadoop.hive.ql.io.avro.AvroContainerInputFormat'
	OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.avro.AvroContainerOutputFormat';

Define the Greenplum Database external table:

postgres=# CREATE EXTERNAL TABLE userinfo_hiveavro(id int, name text, user_id text)
	LOCATION ('pxf://default.hive_avro_data_table?profile=hive')
	FORMAT 'custom' (FORMATTER='pxfwritable_import');

And query the table:

postgres=# SELECT * FROM userinfo_hiveavro;

Working with Complex Data Types

Example: Using the hive Profile with Complex Data Types

This example employs the hive profile and the array and map complex types, specifically an array of integers and a string key/value pair map.

The data schema for this example includes fields with the following names and data types:

Column Name Data Type
index int
name string
intarray array of integers
propmap map of string key and value pairs

When you specify an array field in a Hive table, you must identify the terminator for each item in the collection. Similarly, you must also specify the map key termination character.

  1. Create a text file from which you will load the data set:

    $ vi /tmp/pxf_hive_complex.txt

  2. Add the following text to pxf_hive_complex.txt. This data uses a comma (,) to separate field values, the percent symbol % to separate collection items, and a : to terminate map key values:

    3,Prague,1%2%3,zone:euro%status:up
89,Rome,4%5%6,zone:euro
400,Bangalore,7%8%9,zone:apac%status:pending
183,Beijing,0%1%2,zone:apac
94,Sacramento,3%4%5,zone:noam%status:down
101,Paris,6%7%8,zone:euro%status:up
56,Frankfurt,9%0%1,zone:euro
202,Jakarta,2%3%4,zone:apac%status:up
313,Sydney,5%6%7,zone:apac%status:pending
76,Atlanta,8%9%0,zone:noam%status:down

  3. Create a Hive table to represent this data:

    $ HADOOP_USER_NAME=hdfs hive

    hive> CREATE TABLE table_complextypes( index int, name string, intarray ARRAY<int>, propmap MAP<string, string>)
         ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
         COLLECTION ITEMS TERMINATED BY '%'
         MAP KEYS TERMINATED BY ':'
         STORED AS TEXTFILE;

    Notice that:

    • FIELDS TERMINATED BY identifies a comma as the field terminator.
    • The COLLECTION ITEMS TERMINATED BY subclause specifies the percent sign as the collection items (array item, map key/value pair) terminator.
    • MAP KEYS TERMINATED BY identifies a colon as the terminator for map keys.

  4. Load the pxf_hive_complex.txt sample data file into the table_complextypes table that you just created:

    hive> LOAD DATA LOCAL INPATH '/tmp/pxf_hive_complex.txt' INTO TABLE table_complextypes;

  5. Perform a query on Hive table table_complextypes to verify that the data was loaded successfully:

    hive> SELECT * FROM table_complextypes;

    3	Prague	[1,2,3]	{"zone":"euro","status":"up"}
89	Rome	[4,5,6]	{"zone":"euro"}
400	Bangalore	[7,8,9]	{"zone":"apac","status":"pending"}
...

  6. Use the PXF hive profile to create a readable Greenplum Database external table that references the Hive table named table_complextypes:

    postgres=# CREATE EXTERNAL TABLE complextypes_hiveprofile(index int, name text, intarray text, propmap text)
             LOCATION ('pxf://table_complextypes?PROFILE=hive')
           FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');

    Notice that the integer array and map complex types are mapped to Greenplum Database data type text.

  7. Query the external table:

    postgres=# SELECT * FROM complextypes_hiveprofile;

     index |    name    | intarray |              propmap
-------+------------+----------+------------------------------------
     3 | Prague     | [1,2,3]  | {"zone":"euro","status":"up"}
    89 | Rome       | [4,5,6]  | {"zone":"euro"}
   400 | Bangalore  | [7,8,9]  | {"zone":"apac","status":"pending"}
   183 | Beijing    | [0,1,2]  | {"zone":"apac"}
    94 | Sacramento | [3,4,5]  | {"zone":"noam","status":"down"}
   101 | Paris      | [6,7,8]  | {"zone":"euro","status":"up"}
    56 | Frankfurt  | [9,0,1]  | {"zone":"euro"}
   202 | Jakarta    | [2,3,4]  | {"zone":"apac","status":"up"}
   313 | Sydney     | [5,6,7]  | {"zone":"apac","status":"pending"}
    76 | Atlanta    | [8,9,0]  | {"zone":"noam","status":"down"}
(10 rows)

    intarray and propmap are each serialized as text strings.

Example: Using the hive:orc Profile with Complex Data Types

In the following example, you will create and populate a Hive table stored in ORC format. You will use the hive:orc profile to query the complex types in this Hive table.

  1. Create a Hive table with ORC storage format:

    $ HADOOP_USER_NAME=hdfs hive

    hive> CREATE TABLE table_complextypes_ORC( index int, name string, intarray ARRAY<int>, propmap MAP<string, string>)
        ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
        COLLECTION ITEMS TERMINATED BY '%'
        MAP KEYS TERMINATED BY ':'
      STORED AS ORC;

  2. Insert the data from the table_complextypes table that you created in the previous example into table_complextypes_ORC:

    hive> INSERT INTO TABLE table_complextypes_ORC SELECT * FROM table_complextypes;

    A copy of the sample data set is now stored in ORC format in table_complextypes_ORC.

  3. Perform a Hive query on table_complextypes_ORC to verify that the data was loaded successfully:

    hive> SELECT * FROM table_complextypes_ORC;

    OK
3       Prague       [1,2,3]    {"zone":"euro","status":"up"}
89      Rome         [4,5,6]    {"zone":"euro"}
400     Bangalore    [7,8,9]    {"zone":"apac","status":"pending"}
...

  4. Start the psql subsystem:

    $ psql -d postgres

  5. Use the PXF hive:orc profile to create a readable Greenplum Database external table from the Hive table named table_complextypes_ORC you created in Step 1. The FORMAT clause must specify 'CUSTOM'. The hive:orc CUSTOM format supports only the built-in 'pxfwritable_import' formatter.

    postgres=> CREATE EXTERNAL TABLE complextypes_hiveorc(index int, name text, intarray text, propmap text)
           LOCATION ('pxf://default.table_complextypes_ORC?PROFILE=hive:orc')
             FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');

    Notice that the integer array and map complex types are again mapped to Greenplum Database data type text.

  6. Query the external table:

    postgres=> SELECT * FROM complextypes_hiveorc;

     index |    name    | intarray |              propmap               
-------+------------+----------+------------------------------------
     3 | Prague     | [1,2,3]  | {"zone":"euro","status":"up"}
    89 | Rome       | [4,5,6]  | {"zone":"euro"}
   400 | Bangalore  | [7,8,9]  | {"zone":"apac","status":"pending"}
 ...

    intarray and propmap are again serialized as text strings.

Partition Pruning

The PXF Hive connector supports Hive partition pruning and the Hive partition directory structure. This enables partition exclusion on selected HDFS files comprising a Hive table. To use the partition filtering feature to reduce network traffic and I/O, run a query on a PXF external table using a WHERE clause that refers to a specific partition column in a partitioned Hive table.

The PXF Hive Connector partition filtering support for Hive string and integral types is described below:

  • The relational operators =, <, <=, >, >=, and <> are supported on string types.
  • The relational operators = and <> are supported on integral types (To use partition filtering with Hive integral types, you must update the Hive configuration as described in the Prerequisites).
  • The logical operators AND and OR are supported when used with the relational operators mentioned above.
  • The LIKE string operator is not supported.

To take advantage of PXF partition filtering pushdown, the Hive and PXF partition field names must be the same. Otherwise, PXF ignores partition filtering and the filtering is performed on the Greenplum Database side, impacting performance.

Note: The PXF Hive connector filters only on partition columns, not on other table attributes. Additionally, filter pushdown is supported only for those data types and operators identified above.

PXF filter pushdown is enabled by default. You configure PXF filter pushdown as described in About Filter Pushdown.

Example: Using the hive Profile to Access Partitioned Homogenous Data

In this example, you use the hive profile to query a Hive table named sales_part that you partition on the delivery_state and delivery_city fields. You then create a Greenplum Database external table to query sales_part. The procedure includes specific examples that illustrate filter pushdown.

  1. Create a Hive table named sales_part with two partition columns, delivery_state and delivery_city:

    hive> CREATE TABLE sales_part (cname string, itype string, supplier_key int, price double)
        PARTITIONED BY (delivery_state string, delivery_city string)
        ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';

  2. Load data into this Hive table and add some partitions:

    hive> INSERT INTO TABLE sales_part
        PARTITION(delivery_state = 'CALIFORNIA', delivery_city = 'Fresno')
        VALUES ('block', 'widget', 33, 15.17);
hive> INSERT INTO TABLE sales_part
        PARTITION(delivery_state = 'CALIFORNIA', delivery_city = 'Sacramento')
        VALUES ('cube', 'widget', 11, 1.17);
hive> INSERT INTO TABLE sales_part
        PARTITION(delivery_state = 'NEVADA', delivery_city = 'Reno')
        VALUES ('dowel', 'widget', 51, 31.82);
hive> INSERT INTO TABLE sales_part
        PARTITION(delivery_state = 'NEVADA', delivery_city = 'Las Vegas')
        VALUES ('px49', 'pipe', 52, 99.82);

  3. Query the sales_part table:

    hive> SELECT * FROM sales_part;

    A SELECT * statement on a Hive partitioned table shows the partition fields at the end of the record.

  4. Examine the Hive/HDFS directory structure for the sales_part table:

    $ sudo -u hdfs hdfs dfs -ls -R /apps/hive/warehouse/sales_part
/apps/hive/warehouse/sales_part/delivery_state=CALIFORNIA/delivery_city=Fresno/
/apps/hive/warehouse/sales_part/delivery_state=CALIFORNIA/delivery_city=Sacramento/
/apps/hive/warehouse/sales_part/delivery_state=NEVADA/delivery_city=Reno/
/apps/hive/warehouse/sales_part/delivery_state=NEVADA/delivery_city=Las Vegas/

  5. Create a PXF external table to read the partitioned sales_part Hive table. To take advantage of partition filter push-down, define fields corresponding to the Hive partition fields at the end of the CREATE EXTERNAL TABLE attribute list.

    $ psql -d postgres

    postgres=# CREATE EXTERNAL TABLE pxf_sales_part(
             cname TEXT, itype TEXT,
             supplier_key INTEGER, price DOUBLE PRECISION,
             delivery_state TEXT, delivery_city TEXT)
           LOCATION ('pxf://sales_part?PROFILE=hive')
           FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');

  6. Query the table:

    postgres=# SELECT * FROM pxf_sales_part;

  7. Perform another query (no pushdown) on pxf_sales_part to return records where the delivery_city is Sacramento and  cname is cube:

    postgres=# SELECT * FROM pxf_sales_part WHERE delivery_city = 'Sacramento' AND cname = 'cube';

    The query filters the delivery_city partition Sacramento. The filter on cname is not pushed down, since it is not a partition column. It is performed on the Greenplum Database side after all the data in the Sacramento partition is transferred for processing.

  8. Query (with pushdown) for all records where delivery_state is CALIFORNIA:

    postgres=# SET gp_external_enable_filter_pushdown=on;
postgres=# SELECT * FROM pxf_sales_part WHERE delivery_state = 'CALIFORNIA';

    This query reads all of the data in the CALIFORNIA delivery_state partition, regardless of the city.

Example: Using the hive Profile to Access Partitioned Heterogeneous Data

You can use the PXF hive profile with any Hive file storage types. With the hive profile, you can access heterogeneous format data in a single Hive table where the partitions may be stored in different file formats.

In this example, you create a partitioned Hive external table. The table is composed of the HDFS data files associated with the sales_info (text format) and sales_info_rcfile (RC format) Hive tables that you created in previous exercises. You will partition the data by year, assigning the data from sales_info to the year 2013, and the data from sales_info_rcfile to the year 2016. (Ignore at the moment the fact that the tables contain the same data.) You will then use the PXF hive profile to query this partitioned Hive external table.

  1. Create a Hive external table named hive_multiformpart that is partitioned by a string field named year:

    $ HADOOP_USER_NAME=hdfs hive

    hive> CREATE EXTERNAL TABLE hive_multiformpart( location string, month string, number_of_orders int, total_sales double)
        PARTITIONED BY( year string )
        ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';

  2. Describe the sales_info and sales_info_rcfile tables, noting the HDFS file location for each table:

    hive> DESCRIBE EXTENDED sales_info;
hive> DESCRIBE EXTENDED sales_info_rcfile;

  3. Create partitions in the hive_multiformpart table for the HDFS file locations associated with each of the sales_info and sales_info_rcfile tables:

    hive> ALTER TABLE hive_multiformpart ADD PARTITION (year = '2013') LOCATION 'hdfs://namenode:8020/apps/hive/warehouse/sales_info';
hive> ALTER TABLE hive_multiformpart ADD PARTITION (year = '2016') LOCATION 'hdfs://namenode:8020/apps/hive/warehouse/sales_info_rcfile';

  4. Explicitly identify the file format of the partition associated with the sales_info_rcfile table:

    hive> ALTER TABLE hive_multiformpart PARTITION (year='2016') SET FILEFORMAT RCFILE;

    You need not specify the file format of the partition associated with the sales_info table, as TEXTFILE format is the default.

  5. Query the hive_multiformpart table:

    hive> SELECT * from hive_multiformpart;
...
Bangalore	Jul	271	8320.55	2016
Beijing	Dec	100	4248.41	2016
Prague	Jan	101	4875.33	2013
Rome	Mar	87	1557.39	2013
...
hive> SELECT * from hive_multiformpart WHERE year='2013';
hive> SELECT * from hive_multiformpart WHERE year='2016';

  6. Show the partitions defined for the hive_multiformpart table and exit hive:

    hive> SHOW PARTITIONS hive_multiformpart;
year=2013
year=2016
hive> quit;

  7. Start the psql subsystem:

    $ psql -d postgres

  8. Use the PXF hive profile to create a readable Greenplum Database external table that references the Hive hive_multiformpart external table that you created in the previous steps:

    postgres=# CREATE EXTERNAL TABLE pxf_multiformpart(location text, month text, number_of_orders int, total_sales float8, year text)
             LOCATION ('pxf://default.hive_multiformpart?PROFILE=hive')
           FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');

  9. Query the PXF external table:

    postgres=# SELECT * FROM pxf_multiformpart;

       location    | month | number_of_orders | total_sales | year 
---------------+-------+------------------+-------------+--------
 ....
 Prague        | Dec   |              333 |     9894.77 | 2013
 Bangalore     | Jul   |              271 |     8320.55 | 2013
 Beijing       | Dec   |              100 |     4248.41 | 2013
 Prague        | Jan   |              101 |     4875.33 | 2016
 Rome          | Mar   |               87 |     1557.39 | 2016
 Bangalore     | May   |              317 |     8936.99 | 2016
 ....

  10. Perform a second query to calculate the total number of orders for the year 2013:

    postgres=# SELECT sum(number_of_orders) FROM pxf_multiformpart WHERE month='Dec' AND year='2013';
 sum 
-----
 433

Using PXF with Hive Default Partitions

This topic describes a difference in query results between Hive and PXF queries when Hive tables use a default partition. When dynamic partitioning is enabled in Hive, a partitioned table may store data in a default partition. Hive creates a default partition when the value of a partitioning column does not match the defined type of the column (for example, when a NULL value is used for any partitioning column). In Hive, any query that includes a filter on a partition column excludes any data that is stored in the table’s default partition.

Similar to Hive, PXF represents a table’s partitioning columns as columns that are appended to the end of the table. However, PXF translates any column value in a default partition to a NULL value. This means that a Greenplum Database query that includes an IS NULL filter on a partitioning column can return different results than the same Hive query.

Consider a Hive partitioned table that is created with the statement:

hive> CREATE TABLE sales (order_id bigint, order_amount float) PARTITIONED BY (xdate date);

The table is loaded with five rows that contain the following data:

1.0    1900-01-01
2.2    1994-04-14
3.3    2011-03-31
4.5    NULL
5.0    2013-12-06

Inserting row 4 creates a Hive default partition, because the partition column xdate contains a null value.

In Hive, any query that filters on the partition column omits data in the default partition. For example, the following query returns no rows:

hive> SELECT * FROM sales WHERE xdate IS null;

However, if you map this Hive table to a PXF external table in Greenplum Database, all default partition values are translated into actual NULL values. In Greenplum Database, running the same query against the PXF external table returns row 4 as the result, because the filter matches the NULL value.

Keep this behavior in mind when you run IS NULL queries on Hive partitioned tables.

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