Hadoop (HDFS) Foreign Data Wrapper for PostgreSQL

This PostgreSQL extension implements a Foreign Data Wrapper (FDW) for Hadoop (HDFS).

Please note that this version of hdfs_fdw works with PostgreSQL and EDB Postgres Advanced Server 12, 13, 14, 15, and 16.

Installation

See the file INSTALL for instructions on how to build and install the extension and it's dependencies.

What Is Apache Hadoop?

The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. The detail information can be found here. Hadoop can be downloaded from this location, and can be installed by following the steps given here.

What Is Apache Hive?

The Apache Hive ™ data warehouse software facilitates querying and managing large datasets residing in distributed storage. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL.

There are two version of Hive HiveServer1 and HiveServer2 which can be downloaded from this site. The FDW supports only HiveServer2.

What Is Apache Spark?

The Apache Spark ™ is a general purpose distributed computing framework which supports a wide variety of use cases. It provides real time stream as well as batch processing with speed, ease of use and sophisticated analytics. Spark does not provide storage layer, it relies on third party storage providers like Hadoop, HBASE, Cassandra, S3 etc. Spark integrates seamlessly with Hadoop and can process existing data. Spark SQL is 100% compatible with HiveQL and can be used as a replacement of HiveServer2, using Spark Thrift Server.

Authentication Support

The FDW supports NOSASL and LDAP authentication modes. In order to use NOSASL do not specify any OPTIONS while creating user mapping. For LDAP username and password must be specified in OPTIONS while creating user mapping.

Usage

While creating the foreign server object for HDFS FDW the following can be specified in options:

• host: IP Address or hostname of the Hive Thrift Server OR Spark Thrift Server. Defaults to localhost.
• port: Port number of the Hive Thrift Server OR Spark Thrift Server. Defaults to 10000.
• client_type: hiveserver2 or spark. Hive and Spark both support HiveQL and are compatible but there are few differences like the behaviour of ANALYZE command and connection string for the NOSASL case. Default is hiveserver2.
• auth_type: NOSASL or LDAP. Specify which authentication type is required while connecting to the Hive or Spark server. Default is unspecified and the FDW uses the username option in the user mapping to infer the auth_type. If the username is empty or not specified it uses NOSASL.
• connect_timeout: Connection timeout, default value is 300 seconds.
• query_timeout: Query timeout is not supported by the Hive JDBC driver.
• fetch_size: A user-specified value that is provided as a parameter to the JDBC API setFetchSize. The default value is 10000.
• log_remote_sql: If true, logging will include SQL commands executed on the remote hive server and the number of times that a scan is repeated. The default is false.
• use_remote_estimate: Include the use_remote_estimate to instruct the server to use EXPLAIN commands on the remote server when estimating processing costs. By default, use_remote_estimate is false, and remote tables are assumed to have 1000 rows.
• enable_join_pushdown: If true, pushes the join between two foreign tables from the same foreign server, instead of fetching all the rows for both the tables and performing a join locally. This option can also be set for an individual table, and if any of the tables involved in the join has set it to false then the join will not be pushed down. The table-level value of the option takes precedence over the server-level option value. Default is true.
• enable_aggregate_pushdown: If true, pushes aggregate operations to the foreign server instead of performing them locally. This option can also be set for an individual table, and if any of the tables involved in the query has set it to false then the operations will not be pushed down. The table-level value of the option takes precedence over the server-level option value. Default is true.
• enable_order_by_pushdown: If true, pushes the ORDER BY clause to the foreign server instead of performing a sort locally. This option can also be set for an individual table, and if any of the tables involved in the query has set it to false then the ORDER BY will not be pushed down. The table-level value of the option takes precedence over the server-level option value. Default is true.

When creating user mapping following options can be provided:

• username: The name of the user for authentication on the Hive server.
• password: The password of the user for authentication on the Hive server.

HDFS can be used through either Hive or Spark. In this case both Hive and Spark store metadata in the configured metastore. In the metastore databases and tables can be created using HiveQL. While creating foreign table object for the foreign server the following can be specified in options:

• dbname: Name of the metastore database to query. Default is 'default'.
• table_name: Name of the metastore table. Default is the same as foreign table name.
• enable_join_pushdown: Similar to the server-level option, but can be configured at table level as well. Default is true.
• enable_aggregate_pushdown: Similar to the server-level option, but can be configured at table level as well. Default is true.
• enable_order_by_pushdown: Similar to the server-level option, but can be configured at table level as well. Default is true.

GUC variables:

• hdfs_fdw.enable_join_pushdown: If true, pushes the join between two foreign tables from the same foreign server, instead of fetching all the rows for both the tables and performing a join locally. Default is true.

• hdfs_fdw.enable_aggregate_pushdown: If true, pushes aggregate operations to the foreign server, instead of fetching rows from the foreign server and performing the operations locally. Default is true.

• hdfs_fdw.enable_order_by_pushdown: If true, pushes the order by operation to the foreign server, instead of fetching rows from the foreign server and performing the sort locally. Default is false.

• hdfs_fdw.enable_limit_pushdown: If true, pushes the LIMIT/OFFSET clause to the foreign server. In Apache Spark, support for the OFFSET clause has been introduced in 3.4.1. So for versions < 3.4.1 the option either needs to be disabled or the OFFSET clause should not be used. Default is true.

Using HDFS FDW with Apache Hive on top of Hadoop

Step 1: Download weblogs_parse and follow instructions from this site.

Step 2: Upload weblog_parse.txt file using these commands:

hadoop fs -mkdir /weblogs
hadoop fs -mkdir /weblogs/parse
hadoop fs -put weblogs_parse.txt /weblogs/parse/part-00000

Step 3: Start HiveServer if not already running using following command:

$HIVE_HOME/bin/hiveserver2

or

$HIVE_HOME/bin/hive --service hiveserver2

Step 4: Connect to HiveServer2 using hive beeline client.

e.g.

$ beeline
Beeline version 1.0.1 by Apache Hive
beeline> !connect jdbc:hive2://localhost:10000/default;auth=noSasl

Step 5: Create Table in Hive

CREATE TABLE weblogs
	(
		client_ip           STRING,
		full_request_date   STRING,
		day                 STRING,
		month               STRING,
		month_num           INT,
		year                STRING,
		hour                STRING,
		minute              STRING,
		second              STRING,
		timezone            STRING,
		http_verb           STRING,
		uri                 STRING,
		http_status_code    STRING,
		bytes_returned      STRING,
		referrer            STRING,
		user_agent          STRING
	)
	row format delimited
	fields terminated by '\t';

Step 6: Load data in weblogs table:

hadoop fs -cp /weblogs/parse/part-00000 /user/hive/warehouse/weblogs/

Step 7: Access data from PostgreSQL:

Now we are ready to use the the weblog table in PostgreSQL, we need to follow these steps once we are connected using psql:

-- set the GUC variables appropriately, e.g. :
hdfs_fdw.jvmpath='/home/edb/Projects/hadoop_fdw/jdk1.8.0_111/jre/lib/amd64/server/'
hdfs_fdw.classpath='/usr/local/edbas/lib/postgresql/HiveJdbcClient-1.0.jar:
                    /home/edb/Projects/hadoop_fdw/hadoop/share/hadoop/common/hadoop-common-2.6.4.jar:
                    /home/edb/Projects/hadoop_fdw/apache-hive-1.0.1-bin/lib/hive-jdbc-1.0.1-standalone.jar'

-- load extension first time after install
CREATE EXTENSION hdfs_fdw;

-- create server object
CREATE SERVER hdfs_server
	FOREIGN DATA WRAPPER hdfs_fdw
	OPTIONS (host '127.0.0.1');

-- create user mapping
CREATE USER MAPPING FOR postgres
	SERVER hdfs_server OPTIONS (username 'hive_username', password 'hive_password');

-- create foreign table
CREATE FOREIGN TABLE weblogs
	(
		client_ip                TEXT,
		full_request_date        TEXT,
		day                      TEXT,
		month                    TEXT,
		month_num                INTEGER,
		year                     TEXT,
		hour                     TEXT,
		minute                   TEXT,
		second                   TEXT,
		timezone                 TEXT,
		http_verb                TEXT,
		uri                      TEXT,
		http_status_code         TEXT,
		bytes_returned           TEXT,
		referrer                 TEXT,
		user_agent               TEXT
	)
	SERVER hdfs_server
	OPTIONS (dbname 'default', table_name 'weblogs');

-- select from table
SELECT DISTINCT client_ip IP, count(*)
	FROM weblogs GROUP BY IP HAVING count(*) > 5000 ORDER BY 1;
       ip        | count 
-----------------+-------
 13.53.52.13     |  5494
 14.323.74.653   | 16194
 322.6.648.325   | 13242
 325.87.75.336   |  6500
 325.87.75.36    |  6498
 361.631.17.30   | 64979
 363.652.18.65   | 10561
 683.615.622.618 | 13505
(8 rows)

-- EXPLAIN output showing WHERE clause being pushed down to remote server.
EXPLAIN (VERBOSE, COSTS OFF)
	SELECT client_ip, full_request_date, uri FROM weblogs
	WHERE http_status_code = 200;
                                                   QUERY PLAN                                                   
----------------------------------------------------------------------------------------------------------------
 Foreign Scan on public.weblogs
   Output: client_ip, full_request_date, uri
   Remote SQL: SELECT client_ip, full_request_date, uri FROM default.weblogs WHERE ((http_status_code = '200'))
(3 rows)

Using HDFS FDW with Apache Spark on top of Hadoop

Step 1: Download & install Apache Spark in local mode.

Step 2: In the folder $SPARK_HOME/conf create a file spark-defaults.conf containing the following line

spark.sql.warehouse.dir hdfs://localhost:9000/user/hive/warehouse

By default spark uses derby for both meta data and the data itself (called warehouse in spark). In order to have spark use hadoop as warehouse we have to add this property.

Step 3: Start Spark Thrift Server

./start-thriftserver.sh

Step 4: Make sure Spark thrift server is running using log file

Step 5: Create a local file names.txt with below data:

$ cat /tmp/names.txt
1,abcd
2,pqrs
3,wxyz
4,a_b_c
5,p_q_r
,

Step 6: Connect to Spark Thrift Server2 using spark beeline client.

e.g.

$ beeline
Beeline version 1.2.1.spark2 by Apache Hive
beeline> !connect jdbc:hive2://localhost:10000/default;auth=noSasl org.apache.hive.jdbc.HiveDriver

Step 7: Getting the sample data ready on spark:

Run the following commands in beeline command line tool:-

./beeline
Beeline version 1.2.1.spark2 by Apache Hive
beeline> !connect jdbc:hive2://localhost:10000/default;auth=noSasl org.apache.hive.jdbc.HiveDriver
Connecting to jdbc:hive2://localhost:10000/default;auth=noSasl
Enter password for jdbc:hive2://localhost:10000/default;auth=noSasl: 
Connected to: Spark SQL (version 2.1.1)
Driver: Hive JDBC (version 1.2.1.spark2)
Transaction isolation: TRANSACTION_REPEATABLE_READ
0: jdbc:hive2://localhost:10000> create database my_test_db;
+---------+--+
| Result  |
+---------+--+
+---------+--+
No rows selected (0.379 seconds)
0: jdbc:hive2://localhost:10000> use my_test_db;
+---------+--+
| Result  |
+---------+--+
+---------+--+
No rows selected (0.03 seconds)
0: jdbc:hive2://localhost:10000> create table my_names_tab(a int, name string)
                                 row format delimited fields terminated by ' ';
+---------+--+
| Result  |
+---------+--+
+---------+--+
No rows selected (0.11 seconds)
0: jdbc:hive2://localhost:10000>

0: jdbc:hive2://localhost:10000> load data local inpath '/tmp/names.txt'
                                 into table my_names_tab;
+---------+--+
| Result  |
+---------+--+
+---------+--+
No rows selected (0.33 seconds)
0: jdbc:hive2://localhost:10000> select * from my_names_tab;
+-------+---------+--+
|   a   |  name   |
+-------+---------+--+
| 1     | abcd    |
| 2     | pqrs    |
| 3     | wxyz    |
| 4     | a_b_c   |
| 5     | p_q_r   |
| NULL  | NULL    |
+-------+---------+--+

Here are the corresponding files in hadoop:

$ hadoop fs -ls /user/hive/warehouse/
Found 1 items
drwxrwxrwx   - org.apache.hive.jdbc.HiveDriver supergroup          0 2020-06-12 17:03 /user/hive/warehouse/my_test_db.db

$ hadoop fs -ls /user/hive/warehouse/my_test_db.db/
Found 1 items
drwxrwxrwx   - org.apache.hive.jdbc.HiveDriver supergroup          0 2020-06-12 17:03 /user/hive/warehouse/my_test_db.db/my_names_tab

Step 8: Access data from PostgreSQL:

Connect to Postgres using psql:

-- set the GUC variables appropriately, e.g. :
hdfs_fdw.jvmpath='/home/edb/Projects/hadoop_fdw/jdk1.8.0_111/jre/lib/amd64/server/'
hdfs_fdw.classpath='/usr/local/edbas/lib/postgresql/HiveJdbcClient-1.0.jar:
                    /home/edb/Projects/hadoop_fdw/hadoop/share/hadoop/common/hadoop-common-2.6.4.jar:
                    /home/edb/Projects/hadoop_fdw/apache-hive-1.0.1-bin/lib/hive-jdbc-1.0.1-standalone.jar'

-- load extension first time after install
CREATE EXTENSION hdfs_fdw;

-- create server object
CREATE SERVER hdfs_server
	FOREIGN DATA WRAPPER hdfs_fdw
	OPTIONS (host '127.0.0.1', port '10000', client_type 'spark', auth_type 'NOSASL');

-- create user mapping
CREATE USER MAPPING FOR postgres
	SERVER hdfs_server OPTIONS (username 'spark_username', password 'spark_password');

-- create foreign table
CREATE FOREIGN TABLE f_names_tab( a int, name varchar(255)) SERVER hdfs_svr
	OPTIONS (dbname 'testdb', table_name 'my_names_tab');

-- select the data from foreign server
SELECT * FROM f_names_tab;
 a |  name 
---+--------
 1 | abcd
 2 | pqrs
 3 | wxyz
 4 | a_b_c
 5 | p_q_r
 0 |
(6 rows)

-- EXPLAIN output showing WHERE clause being pushed down to remote server.
EXPLAIN (verbose, costs off)
	SELECT name FROM f_names_tab
	WHERE a > 3;
                                QUERY PLAN                                
--------------------------------------------------------------------------
 Foreign Scan on public.f_names_tab
   Output: name
   Remote SQL: SELECT name FROM my_test_db.my_names_tab WHERE ((a > '3'))
(3 rows)

Please note that we are using the same port while creating foreign server because Spark Thrift Server is compatible with Hive Thrift Server. Applications using Hiveserver2 would work with Spark except for the behaviour of ANALYZE command and the connection string in case of NOSASL. It is better to use ALTER SERVER and change the client_type option if Hive is to be replaced with Spark.

For more details, please refer to hdfs_fdw documentation.

Contributing

If you experience any bug create new issue and if you have fix for that create a pull request. Before submitting a bug-fix or new feature, please read the contributing guidelines.

Support

This project will be modified to maintain compatibility with new PostgreSQL and EDB Postgres Advanced Server releases.

If you require commercial support, please contact the EnterpriseDB sales team, or check whether your existing PostgreSQL support provider can also support hdfs_fdw.

Copyright Information

Copyright (c) 2011-2024, EnterpriseDB Corporation.

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