1. Oracle GoldenGate for Big Data
  2. Configure
  3. Configuring Oracle GoldenGate for Big Data

6.1 Configuring Oracle GoldenGate for Big Data

This topic describes how to configure Oracle GoldenGate for Big Data Handlers.

Parent topic: Configure

6.1.1 Running with Replicat

You need to run the Java Adapter with the Oracle GoldenGate Replicat process to begin configuring Oracle GoldenGate for Big Data.

This topic explains how to run the Java Adapter with the Oracle GoldenGate Replicat process.

Parent topic: Configuring Oracle GoldenGate for Big Data

6.1.1.1 Configuring Replicat

The following is an example of how you can configure a Replicat process properties file for use with the Java Adapter:

REPLICAT hdfs
TARGETDB LIBFILE libggjava.so SET property=dirprm/hdfs.properties 
--SOURCEDEFS ./dirdef/dbo.def 
DDL INCLUDE ALL
GROUPTRANSOPS 1000
MAPEXCLUDE dbo.excludetable
MAP dbo.*, TARGET dbo.*;

The following is explanation of these Replicat configuration entries:

REPLICAT hdfs - The name of the Replicat process.

TARGETDB LIBFILE libggjava.so SET property=dirprm/hdfs.properties - Sets the target database as you exit to libggjava.so and sets the Java Adapters property file to dirprm/hdfs.properties.

--SOURCEDEFS ./dirdef/dbo.def - Sets a source database definitions file. It is commented out because Oracle GoldenGate trail files provide metadata in trail.

GROUPTRANSOPS 1000 - Groups 1000 transactions from the source trail files into a single target transaction. This is the default and improves the performance of Big Data integrations.

MAPEXCLUDE dbo.excludetable - Sets the tables to exclude.

MAP dbo.*, TARGET dbo.*; - Sets the mapping of input to output tables.

Parent topic: Running with Replicat

6.1.1.2 Adding the Replicat Process

The command to add and start the Replicat process in ggsci is the following: 

ADD REPLICAT hdfs, EXTTRAIL ./dirdat/gg
START hdfs

Parent topic: Running with Replicat

6.1.1.3 Replicat Grouping

The Replicat process provides the Replicat configuration property, GROUPTRANSOPS, to control transaction grouping. By default, the Replicat process implements transaction grouping of 1000 source transactions into a single target transaction. If you want to turn off transaction grouping then the GROUPTRANSOPS Replicat property should be set to 1.

Parent topic: Running with Replicat

6.1.1.4 About Replicat Checkpointing

In addition to the Replicat checkpoint file ,.cpr, an additional checkpoint file, dirchk/group.cpj, is created that contains information similar to CHECKPOINTTABLE in Replicat for the database.

Parent topic: Running with Replicat

6.1.1.5 About Initial Load Support

Replicat can already read trail files that come from both the online capture and initial load processes that write to a set of trail files. In addition, Replicat can also be configured to support the delivery of the special run initial load process using RMTTASK specification in the Extract parameter file. For more details about configuring the direct load, see Loading Data with an Oracle GoldenGate Direct Load.

Note:

The SOURCEDB or DBLOGIN parameter specifications vary depending on your source database.

Parent topic: Running with Replicat

6.1.1.6 About the Unsupported Replicat Features

The following Replicat features are not supported in this release:

  • BATCHSQL

  • SQLEXEC

  • Stored procedure

  • Conflict resolution and detection (CDR)

Parent topic: Running with Replicat

6.1.1.7 How the Mapping Functionality Works

The Oracle GoldenGate Replicat process supports mapping functionality to custom target schemas. You must use the Metadata Provider functionality to define a target schema or schemas, and then use the standard Replicat mapping syntax in the Replicat configuration file to define the mapping. For more information about the Replicat mapping syntax in the Replication configuration file, see Mapping and Manipulating Data.

Parent topic: Running with Replicat

6.1.2 About Schema Evolution and Metadata Change Events

The Metadata in trail is a feature that allows seamless runtime handling of metadata change events by Oracle GoldenGate for Big Data, including schema evolution and schema propagation to Big Data target applications. The NO_OBJECTDEFS is a sub-parameter of the Extract and Replicat EXTTRAIL and RMTTRAIL parameters that lets you suppress the important metadata in trail feature and revert to using a static metadata definition.

The Oracle GoldenGate for Big Data Handlers and Formatters provide functionality to take action when a metadata change event is encountered. The ability to take action in the case of metadata change events depends on the metadata change events being available in the source trail file. Oracle GoldenGate supports metadata in trail and the propagation of DDL data from a source Oracle Database. If the source trail file does not have metadata in trail and DDL data (metadata change events) then it is not possible for Oracle GoldenGate for Big Data to provide and metadata change event handling.

Parent topic: Configuring Oracle GoldenGate for Big Data

6.1.3 About Configuration Property CDATA[] Wrapping

The GoldenGate for Big Data Handlers and Formatters support the configuration of many parameters in the Java properties file, the value of which may be interpreted as white space. The configuration handling of the Java Adapter trims white space from configuration values from the Java configuration file. This behavior of trimming whitespace may be desirable for some configuration values and undesirable for other configuration values. Alternatively, you can wrap white space values inside of special syntax to preserve the whites pace for selected configuration variables. GoldenGate for Big Data borrows the XML syntax of CDATA[] to preserve white space. Values that would be considered to be white space can be wrapped inside of CDATA[].

The following is an example attempting to set a new-line delimiter for the Delimited Text Formatter:

gg.handler.{name}.format.lineDelimiter=\n

This configuration will not be successful. The new-line character is interpreted as white space and will be trimmed from the configuration value. Therefore the gg.handler setting effectively results in the line delimiter being set to an empty string.

In order to preserve the configuration of the new-line character simply wrap the character in the CDATA[] wrapper as follows:

gg.handler.{name}.format.lineDelimiter=CDATA[\n]

Configuring the property with the CDATA[] wrapping preserves the white space and the line delimiter will then be a new-line character.

Parent topic: Configuring Oracle GoldenGate for Big Data

6.1.4 Using Regular Expression Search and Replace

You can perform more powerful search and replace operations of both schema data (catalog names, schema names, table names, and column names) and column value data, which are separately configured. Regular expressions (regex) are characters that customize a search string through pattern matching.

You can match a string against a pattern or extract parts of the match. Oracle GoldenGate for Big Data uses the standard Oracle Java regular expressions package, java.util.regex, see Regular Expressions in  The Single UNIX Specification, Version 4.

Parent topic: Configuring Oracle GoldenGate for Big Data

6.1.4.1 Using Schema Data Replace

You can replace schema data using the gg.schemareplaceregex and gg.schemareplacestring properties. Use gg.schemareplaceregex to set a regular expression, and then use it to search catalog names, schema names, table names, and column names for corresponding matches. Matches are then replaced with the content of the gg.schemareplacestring value. The default value of gg.schemareplacestring is an empty string or "".

For example, some system table names start with a dollar sign like $mytable. You may want to replicate these tables even though most Big Data targets do not allow dollar signs in table names. To remove the dollar sign, you could configure the following replace strings: 

gg.schemareplaceregex=[$] 
gg.schemareplacestring=

The resulting example of searched and replaced table name is mytable. These properties also support CDATA[] wrapping to preserve whitespace in the value of configuration values. So the equivalent of the preceding example using CDATA[] wrapping use is: 

gg.schemareplaceregex=CDATA[[$]]
gg.schemareplacestring=CDATA[]

The schema search and replace functionality supports using multiple search regular expressions and replacements strings using the following configuration syntax: 

gg.schemareplaceregex=some_regex
gg.schemareplacestring=some_value
gg.schemareplaceregex1=some_regex
gg.schemareplacestring1=some_value
gg.schemareplaceregex2=some_regex
gg.schemareplacestring2=some_value

Parent topic: Using Regular Expression Search and Replace

6.1.4.2 Using Content Data Replace

You can replace content data using the gg.contentreplaceregex and gg.contentreplacestring properties to search the column values using the configured regular expression and replace matches with the replacement string. For example, this is useful to replace line feed characters in column values. If the delimited text formatter is used then line feeds occurring in the data will be incorrectly interpreted as line delimiters by analytic tools.

You can configure n number of content replacement regex search values. The regex search and replacements are done in the order of configuration. Configured values must follow a given order as follows: 

gg.contentreplaceregex=some_regex 
gg.contentreplacestring=some_value 
gg.contentreplaceregex1=some_regex 
gg.contentreplacestring1=some_value 
gg.contentreplaceregex2=some_regex 
gg.contentreplacestring2=some_value

Configuring a subscript of 3 without a subscript of 2 would cause the subscript 3 configuration to be ignored.

Attention:

 Regular express searches and replacements require computer processing and can reduce the performance of the Oracle GoldenGate for Big Data process.

To replace line feeds with a blank character you could use the following property configurations:

gg.contentreplaceregex=[\n] 
gg.contentreplacestring=CDATA[ ]

This changes the column value from:

this is 
me

to :

this is me

Both values support CDATA wrapping. The second value must be wrapped in a CDATA[] wrapper because a single blank space will be interpreted as whitespace and trimmed by the Oracle GoldenGate for Big Data configuration layer. In addition, you can configure multiple search a replace strings. For example, you may also want to trim leading and trailing white space out of column values in addition to trimming line feeds from: 

^\\s+|\\s+$
gg.contentreplaceregex1=^\\s+|\\s+$ 
gg.contentreplacestring1=CDATA[]

Parent topic: Using Regular Expression Search and Replace

6.1.5 Scaling Oracle GoldenGate for Big Data Delivery

 Oracle GoldenGate for Big Data supports breaking down the source trail files into either multiple Replicat processes or by using Coordinated Delivery to instantiate multiple Java Adapter instances inside a single Replicat process to improve throughput.. This allows you to scale Oracle GoldenGate for Big Data delivery.

There are some cases where the throughput to Oracle GoldenGate for Big Data integration targets is not sufficient to meet your service level agreements even after you have tuned your Handler for maximum performance. When this occurs, you can configure parallel processing and delivery to your targets using one of the following methods:

  • Multiple Replicat processes can be configured to read data from the same source trail files. Each of these Replicat processes are configured to process a subset of the data in the source trail files so that all of the processes collectively process the source trail files in their entirety.  There is no coordination between the separate Replicat processes using this solution.

  • Oracle GoldenGate Coordinated Delivery can be used to parallelize processing the data from the source trail files within a single Replicat process. This solution involves breaking the trail files down into logical subsets for which each configured subset is processed by a different delivery thread. For more information about Coordinated Delivery, see https://blogs.oracle.com/dataintegration/entry/goldengate_12c_coordinated_replicat.

With either method, you can split the data into parallel processing for improved throughput. Oracle recommends breaking the data down in one of the following two ways:

  • Splitting Source Data By Source Table –Data is divided into subsections by source table. For example, Replicat process 1 might handle source tables table1 and table2, while Replicat process 2 might handle data for source tables table3 and table2. Data is split for source table and the individual table data is not subdivided.

  • Splitting Source Table Data into Sub Streams – Data from source tables is split. For example, Replicat process 1 might handle half of the range of data from source table1, while Replicat process 2 might handler the other half of the data from source table1.

Additional limitations:

  • Parallel apply is not supported.

  • The BATCHSQL parameter not supported.

Example 6-1 Scaling Support for the Oracle GoldenGate for Big Data Handlers

Handler Name Splitting Source Data By Source Table Splitting Source Table Data into Sub Streams

Cassandra

Supported

Supported when:

  • Required target tables in Cassandra are pre-created.

  • Metadata change events do not occur.

Elastic Search

Supported

Supported

HBase

Supported when all required HBase namespaces are pre-created in HBase.

Supported when:

  • All required HBase namespaces are pre-created in HBase.

  • All required HBase target tables are pre-created in HBase. Schema evolution is not an issue because HBase tables have no schema definitions so a source metadata change does not require any schema change in HBase.

  • The source data does not contain any truncate operations.

HDFS

Supported

Supported with some restrictions.

  • You must select a naming convention for generated HDFS files wherethe file names do not collide. Colliding HDFS file names results in a Replicat abend. When using coordinated apply it is suggested that you configure ${groupName} as part of the configuration for the gg.handler.name.fileNameMappingTemplate property . The ${groupName} template resolves to the Replicat name concatenated with the Replicat thread number, which provides unique naming per Replicat thread. 

  • Schema propagatation to HDFS and Hive integration is not currently supported.

JDBC

Supported

Supported

Kafka

Supported

Supported for formats that support schema propagation, such as Avro. This is less desirable due to multiple instances feeding the same schema information to the target.

Kafka Connect

Supported

Supported

Kinesis Streams

Supported

Supported

MongoDB

Supported

Supported
Java File Writer Supported Supported with the following restrictions:

You must select a naming convention for generated files where the file names do not collide. Colliding file names may results in a Replicat abend and/or polluted data. When using coordinated apply it is suggested that you configure ${groupName} as part of the configuration for the gg.handler.name.fileNameMappingTemplate property . The ${groupName} template resolves to the Replicat name concatenated with the Replicat thread number, which provides unique naming per Replicat thread.

Parent topic: Configuring Oracle GoldenGate for Big Data

6.1.6 Configuring Cluster High Availability

Oracle GoldenGate for Big Data doesn't have built-in high availability functionality. You need to use a standard cluster software's high availability capability to provide the high availability functionality.

You can configure a high availability scenario on a cluster so that if the leader instance of Oracle GoldenGate for Big Data on machine fails, another Oracle GoldenGate for Big Data instance could be started on another machine to resume where the failed instance left off.

If you manually configure your instances to share common Oracle GoldenGate for Big Data and Oracle GoldenGate files using a shared disk architecture you can create a fail over situation. For a cluster installation, these files would need to accessible from all machines and accessible in the same location.

The configuration files that must be shared are:

  • replicat.prm

  • Handler properties file.

  • Additional properties files required by the specific adapter. This depends on the target handler in use. For example, Kafka would be a producer properties file.

  • Additional schema files you've generated. For example, Avro schema files generated in the dirdef directory.

  • File Writer Handler generated files on your local file system at a configured path. Also, the File Writer Handler state file in the dirsta directory.

  • Any log4j.properties or logback.properties files in use.

Checkpoint files must be shared for the ability to resume processing:

  • Your Replicat checkpoint file (*.cpr).

  • Your adapter checkpoint file (*.cpj).

Parent topic: Configuring Oracle GoldenGate for Big Data

6.1.7 Using Identities in Oracle GoldenGate Credential Store

The Oracle GoldenGate credential store manages user IDs and their encrypted passwords (together known as credentials) that are used by Oracle GoldenGate processes to interact with the local database. The credential store eliminates the need to specify user names and clear-text passwords in the Oracle GoldenGate parameter files.

An optional alias can be used in the parameter file instead of the user ID to map to a userid and password pair in the credential store. The credential store is implemented as an auto login wallet within the Oracle Credential Store Framework (CSF). The use of an LDAP directory is not supported for the Oracle GoldenGate credential store. The auto login wallet supports automated restarts of Oracle GoldenGate processes without requiring human intervention to supply the necessary passwords.

In Oracle GoldenGate for Big Data, you specify the alias and domain in the property file not the actual user ID or password. User credentials are maintained in secure wallet storage.

Parent topic: Configuring Oracle GoldenGate for Big Data

6.1.7.1 Creating a Credential Store

You can create a credential store for your Big Data environment.

Run the GGSCI ADD CREDENTIALSTORE command to create a file called cwallet.sso in the dircrd/ subdirectory of your Oracle GoldenGate installation directory (the default).

You can the location of the credential store (cwallet.sso file by specifying the desired location with the CREDENTIALSTORELOCATION parameter in the GLOBALS file.

For more information about credential store commands, see Reference for Oracle GoldenGate.

Note:

Only one credential store can be used for each Oracle GoldenGate instance.

Parent topic: Using Identities in Oracle GoldenGate Credential Store

6.1.7.2 Adding Users to a Credential Store

After you create a credential store for your Big Data environment, you can added users to the store.

Run the GGSCI ALTER CREDENTIALSTORE ADD USER userid PASSWORD password [ALIAS alias] [DOMAIN domain] command to create each user, where:

  • userid is the user name. Only one instance of a user name can exist in the credential store unless the ALIAS or DOMAIN option is used.

  • password is the user's password. The password is echoed (not obfuscated) when this option is used. If this option is omitted, the command prompts for the password, which is obfuscated as it is typed (recommended because it is more secure).

  • alias is an alias for the user name. The alias substitutes for the credential in parameters and commands where a login credential is required. If the ALIAS option is omitted, the alias defaults to the user name.

For example:

ALTER CREDENTIALSTORE ADD USER scott PASSWORD tiger ALIAS scsm2 domain ggadapters

For more information about credential store commands, see Reference for Oracle GoldenGate.

Parent topic: Using Identities in Oracle GoldenGate Credential Store

6.1.7.3 Configuring Properties to Access the Credential Store

The Oracle GoldenGate Java Adapter properties file requires specific syntax to resolve user name and password entries in the Credential Store at runtime. For resolving a user name the syntax is the following:

ORACLEWALLETUSERNAME[alias domain_name]

For resolving a password the syntax required is the following:

ORACLEWALLETPASSWORD[alias domain_name]

The following example illustrate how to configure a Credential Store entry with an alias of myalias and a domain of mydomain.

Note:

With HDFS Hive JDBC the user name and password is encrypted.

Oracle Wallet integration only works for configuration properties which contain the string username or password. For example:

gg.handler.hdfs.hiveJdbcUsername=ORACLEWALLETUSERNAME[myalias mydomain] 
gg.handler.hdfs.hiveJdbcPassword=ORACLEWALLETPASSWORD[myalias mydomain]

ORACLEWALLETUSERNAME and ORACLEWALLETPASSWORD can be used in the Extract (similar to Replicat) in JMS handler as well. For example: 

gg.handler.<name>.user=ORACLEWALLETUSERNAME[JMS_USR JMS_PWD]
 
gg.handler.<name>.password=ORACLEWALLETPASSWORD[JMS_USR JMS_PWD]

Consider the user name and password entries as accessible values in the Credential Store. Any configuration property resolved in the Java Adapter layer (not accessed in the C user exit layer) can be resolved from the Credential Store. This allows you more flexibility to be creative in how you protect sensitive configuration entries.

Parent topic: Using Identities in Oracle GoldenGate Credential Store