Oracle Multitable Consumer
The Oracle Multitable Consumer origin reads Oracle database data from multiple tables and multiple schemas through a JDBC connection. For information about supported versions, see Supported Systems and Versions in the Data Collector documentation.
Use the origin to read multiple tables from one or more schemas in the same database. For example, you might use the origin to perform database replication.
When you configure the origin, you specify connection information and custom JDBC configuration properties to determine how the origin connects to the database. You can also use a connection to configure the origin.
You define groups of database tables to read. The origin generates SQL queries based on the table configurations that you define, and then returns data as a map with column names and field values.
When you define the table configurations, you can optionally override the default key column and specify the initial offset to use. By default, the origin processes tables incrementally, using primary key columns or user-defined offset columns to track its progress. You can configure the origin to perform non-incremental processing to enable it to also process tables that do not have a key or offset column.
You can configure the origin to perform multithreaded partition processing, multithreaded table processing, or use the default - a mix of both. You also specify the processing batch strategy. When configuring partitions, you can configure the offset size, number of active partitions, and offset conditions.
You can configure advanced properties, such as the initial order to read from tables, connection related properties, and transaction isolation. And you can specify what the origin does when encountering an unsupported data type.
When the pipeline stops, the Oracle Multitable Consumer origin notes where it stops reading. When the pipeline starts again, the origin continues processing from where it stopped by default. You can reset the origin to process all available data, using any initial offsets that you defined.
By default, the origin generates JDBC record header and field attributes that provide additional information about each record and field.
You can configure advanced connection properties. To use a JDBC version older than 4.0, you specify the driver class name and define a health check query.
The origin can generate events for an event stream. For more information about dataflow triggers and the event framework, see Dataflow Triggers Overview.
Before you use the Oracle Multitable Consumer origin, you must complete a prerequisite task.
Prerequisite
Before using the Oracle Multitable Consumer origin, install the Oracle JDBC driver for the database. The origin cannot access the database until you install this driver.
- Download the Oracle JDBC driver from the Oracle website.Note: Writing XML data to Oracle requires installing the Oracle Data Integrator Driver for XML. For more information, see the Oracle documentation.
- Install the driver as an external library into the JDBC branded Oracle stage
library,
streamsets-datacollector-jdbc-branded-oracle-lib
, which includes the origin.
Oracle Data Types
The Oracle Multitable Consumer origin converts Oracle data types into Data Collector data types.
Oracle Data Type | Data Collector Data Type |
---|---|
Number | Decimal |
Char | String |
Varchar, Varchar2 | String |
Nchar, NvarChar2 | String |
Binary_float | Float |
Binary_double | Double |
Date | Datetime |
Timestamp | Datetime |
Timestamp with time zone | Zoned_datetime |
Timestamp with local time zone | Zoned_datetime |
Long | String |
Blob | Byte_array |
Clob | String |
Nclob | String |
XMLType | String |
Unsupported Data Types
- Stops the pipeline
- If the stage encounters an unsupported data type, the stage stops the
pipeline after completing the processing of the previous records and
displays the following
error:
JDBC_37 - Unsupported type 1111 for column.
- Converts to string
- If the stage encounters an unsupported data type, the stage converts the data to string when possible, and then continues processing. Not all unsupported data types can successfully be converted to string. When using this option, verify that the data is converted to string as expected.
Table Configuration
When you configure an Oracle Multitable Consumer origin, you define a table configuration for each group of tables that you want to read. A table configuration defines a group of tables with the same table name pattern, that are from one or more schemas with the same name pattern, and that have proper primary keys or the same user-defined offset columns.
You can define one or more table configurations.
For example, you can define one table configuration to replicate a database that has a
proper primary key for each table. You simply enter the schema name and use the default
table name pattern %
which matches all tables in the schema.
store_a
store_b
store_c
customers
The three store tables use orderID
as the primary key. You want to
override the primary key for the customers table, and so need to define
customerID
as the offset column for that table. You want to read
all available data in the tables, so do not need to define an initial offset value.
- Schema - SALES
- Table Name Pattern - store%
- Schema - SALES
- Table Name Pattern - customers
- Override Offset Columns - enabled
- Offset Columns - customerID
Schema, Table Name, and Exclusion Patterns
You define the group of tables that the Oracle Multitable Consumer origin reads by defining schema and table name patterns for the table configuration. The origin reads all tables with names that match the table pattern in the schemas with names that match the schema pattern.
The schema and table name patterns use the SQL LIKE syntax. For
example, the LIKE syntax uses the percentage wildcard (%) to represent any string of
zero or more characters. The schema name pattern st%
matches schemas
whose names start with st
. The default table name pattern
%
matches all tables in the specified schemas.
For more information about valid patterns for the SQL LIKE syntax, see the Microsoft documentation.
You can optionally define a schema or table exclusion pattern to exclude some schemas or tables from being read. The schema and table exclusion patterns use a Java-based regular expression, or regex. For more information about using regular expressions with Data Collector, see Regular Expressions Overview.
US_WEST
and US_EAST
schemas except for tables that start with
dept
. You enter the following schema, table name pattern, and table
exclusion pattern: - Schema - US%
- Table Name Pattern - %
- Table Exclusion Pattern - dept.*
Since you do not need to exclude any schemas, you simply leave the schema exclusion pattern empty.
sys
and system
schemas. You enter the
following schema, table name pattern, and schema exclusion pattern and leave the table
exclusion pattern blank: - Schema - %
- Table Name Pattern - %
- Schema Exclusion Pattern - sys|system
Offset Column and Value
The Oracle Multitable Consumer origin uses an offset column and initial offset value to determine where to start reading data within tables and partitions.
By default, the origin uses the primary key of the tables as the offset column and uses no initial offset value. When you use multithreaded table processing and the table has a composite primary key, the origin uses each primary key as an offset column. You cannot use composite keys with multithreaded partition processing.
SELECT * FROM <table> ORDER BY <offset column_1>, <offset column_2>, ...
Where <offset column_n>
represents each primary key of the
table, such as when the table has a composite primary key. When you restart the
pipeline or when the origin switches back to a previously read table, the origin
adds a WHERE clause to the SQL query to continue reading from the last saved
offset.
To use this default behavior, you do not need to configure any of the offset properties.
- Override the primary key as the offset column
- You can override the primary key and define another offset column or columns. Or if the table doesn’t have a primary key, you can define the offset column or columns to use.
- Define an initial offset value
- The initial offset value is a value within the offset column where you want the JDBC Multitable Consumer origin to start reading. When you define an initial offset value, you must first enter the offset column name and then the value. If you are using the default primary key as the offset column, enter the name of the primary key.
- Define additional offset column conditions
- You can use the expression language to define additional conditions that the origin uses to determine where to start reading data. The origin adds the defined condition to the WHERE clause of the SQL query.
Multithreaded Processing Modes
The Oracle Multitable Consumer origin performs parallel processing and enables the creation of a multithreaded pipeline. The origin can use multiple threads to process entire tables or partitions within tables.
By default, the origin performs multithreaded partition processing for the tables that fulfill the partition processing requirements, while performing multithreaded table processing for all other tables. When using the default behavior, the origin notes the tables that allow partition processing in the Data Collector log. When needed, you can configure the origin to require partition processing for all tables or to perform only table processing. You can also allow the single-threaded non-incremental processing of tables when needed.
- Multithreaded table processing - The origin can use up to one thread per table. Can process tables with multiple offset columns.
- Multithreaded partition processing - The origin can use up to one
thread per table partition. Use to process larger volumes of data than
multithreaded table processing.
Multithreaded partition processing requires a single primary key or user-defined offset column of a supported data type, and additional details for partition creation. Tables with composite keys or a key or user-defined offset column of an unsupported data type cannot be partitioned.
- Off - Use to perform multithreaded table processing.
Can be used to perform non-incremental loads of tables without key or offset columns, when enabled.
- On (Best Effort) - Use to perform partition processing where possible and allow
multithreaded table processing for tables with multiple key or offset columns.
Can be used to perform non-incremental loads of tables without key or offset columns, when enabled.
- On (Required) - Use to perform partition processing for all specified tables.
Does not allow performing other types of processing for tables that do not meet the partition processing requirements.
Multithreaded Table Processing
As the pipeline runs, each thread connects to the origin system, creates a batch of data, and passes the batch to an available pipeline runner. A pipeline runner is a sourceless pipeline instance - an instance of the pipeline that includes all of the processors, executors, and destinations in the pipeline and handles all pipeline processing after the origin.
Each pipeline runner processes one batch at a time, just like a pipeline that runs on a single thread. When the flow of data slows, the pipeline runners wait idly until they are needed, generating an empty batch at regular intervals. You can configure the Runner Idle Time pipeline property to specify the interval or to opt out of empty batch generation.
Multithreaded pipelines preserve the order of records within each batch, just like a single-threaded pipeline. But since batches are processed by different pipeline runners, the order that batches are written to destinations is not ensured.
The order of batch processing depends on many factors. For more information, see Processing Queue.
For more information about multithreaded pipelines, see Multithreaded Pipeline Overview.
Example
Say you are reading from ten tables. You set the Number of Threads property to 5 and the Maximum Pool Size property to 6. When you start the pipeline, the origin retrieves the list of tables. The origin then creates five threads to read from the first five tables, and by default Data Collector creates a matching number of pipeline runners. Upon receiving data, a thread passes a batch to each of the pipeline runners for processing.
At any given moment, the five pipeline runners can each process a batch, so this multithreaded pipeline processes up to five batches at a time. When incoming data slows, the pipeline runners sit idle, available for use as soon as the data flow increases.
Multithreaded Partition Processing
By default, the Oracle Multitable Consumer origin performs multithreaded partition processing for all tables that meet the partition processing requirements, and performs table processing for all other tables.
To perform multithreaded processing of partitions within a table, you enable partition processing in the table configuration, then specify the partition size and the maximum number of partitions to use. Limiting the number of partitions also limits the number of threads that can be dedicated to processing data in the table.
When you configure a set of tables for unlimited partitions, the origin creates up to twice as many partitions as the pipeline thread count. For example, if you have 5 threads, the table can have up to 10 partitions.
Similar to multithreaded table processing, each thread reads data from a single partition, and each partition can have a maximum of one thread read from it at a time.
When processing partitions, the processing order depends on many factors. For a full description, see Processing Queue.
Partition Processing Requirements
- Single key or offset column
- The table must have a single primary key or user-defined offset column. Performing multithreaded partition processing on a table with composite keys generates an error and stops the pipeline.
- Numeric data type
- To use partition processing, the primary key or user-defined offset column must have a numeric data type that allows arithmetic partitioning.
Multiple Offset Value Handling
When processing partitions, the Oracle Multitable Consumer origin can process multiple
records with the same offset value. For example, the origin can process multiple records
with the same timestamp in a transaction_date
offset column.
When you stop the pipeline as the origin is processing a series of records with the same offset value, the origin notes the offset. Then, when you restart the pipeline, it starts with a record with the next logical offset value, skipping any unprocessed records that use the same last-saved offset.
For example, say you specified a datetime column as a user-defined offset column, and five records in the table share the same datetime value. Now say you happen to stop the pipeline after it processes the second record. The pipeline stores the datetime value as the offset where it stopped. When you restart the pipeline, processing begins with the next datetime value, skipping the three unprocessed records with the last-saved offset value.
Best Effort: Processing Non-Compliant Tables
To process tables in a table configuration that might not meet the partition processing requirements, you can use the On (Best Effort) option when you configure the Multithreaded Partition Processing mode property.
When you select the best effort option, the origin performs multithreaded partition processing for all tables that meet the partition processing requirements. The origin performs multithreaded table processing for tables that include multiple key or offset columns. If you enable non-incremental processing, the origin can also process all tables that do not include key or offset columns.
Non-Incremental Processing
You can configure the Oracle Multitable Consumer origin to perform non-incremental processing for tables with no primary keys or user-defined offset columns. By default, the origin performs incremental processing and does not process tables without a key or offset column.
You can enable non-incremental processing for the set of tables defined in a table configuration on the Tables tab.
Use On (Best Effort) or Off to perform non-incremental processing of the table. With either option selected, table is processed using a single thread, like multithreaded table processing.
- The origin uses a single thread to process all available data in the table.
- After the origin processes all available data, it notes that the table has been
processed as an offset. So, if you stop and restart the pipeline after the
origin completes all processing, the origin does not reprocess the table.
If you want to reprocess data in the table, you can reset the origin before restarting the pipeline. This resets the origin for all tables that the origin processes.
- If the pipeline stops while the origin is still processing available data, when the pipeline restarts, the origin reprocesses the entire table. This occurs because the table has no key or offset column to allow for tracking progress.
For example, say you configure the origin to use five threads and process a set of tables that includes a table with no key or offset column. To process data in this table, you enable the Enable Non-Incremental Load table configuration property. You also set Multithreaded Partition Processing Mode to On (Best Effort) to allow the origin to use multithreaded partition processing when possible and allow both non-incremental processing and multithreaded table processing when needed.
When you start the pipeline, the origin allocates one thread to the table that requires non-incremental processing. It processes the table data using multithreaded table processing until all data is processed. When the thread completes processing all available data, the origin notes this as part of the offset and the thread becomes available to process data from other tables. In the meantime, the four other threads process data from the rest of the tables using multithreaded partition processing when possible.
Batch Strategy
You can specify the batch strategy to use when processing data. The batch strategy behaves differently depending on whether you use multithreaded table processing or multithreaded partition processing.
Process All Available Rows
- Multithreaded table processing
-
When the origin performs multithreaded table processing for all tables, each thread processes all available rows from a table. A thread runs a SQL query and processes all of the results for a table. Then, the thread switches to the next available table.
- Multithreaded partition processing
- Multithreaded partition processing is similar to multithreaded table processing, except that it works at a partition level. Each thread runs a SQL query for a partition and processes multiple batches of data from the results. When all data in the partition is processed, the thread switches to the next available partition.
Switch Tables
- Multithreaded table processing
- When the origin performs multithreaded table processing for all tables, each thread processes one batch of data, then switches to an available table and repeats the process. When a pipeline starts, each thread runs a SQL query, generates a result set, and processes a batch of records from the result set. The database driver caches the remaining records for the same thread to access again. Then, the thread switches to the next available table.
- Multithreaded partition processing
-
Multithreaded partition processing is similar to multithreaded table processing. The behavior around caching the result set and the number of batches to process from the result set is the same, but at a partition level. Threads also skip processing partitions that already have a result set cached from a different thread.
The difference is, when a thread works on a partition, all partitions from the same table are moved to the end of the processing queue. This allows the origin to switch to the next available table.
For examples of how tables and partitions rotate through the processing queue, see Processing Queue.
Initial Table Order Strategy
- None
- Reads the tables in the order that they are listed in the database.
- Alphabetical
- Reads the tables in alphabetical order.
- Referential Constraints
- Reads the tables based on the dependencies between the tables. The origin reads the parent table first, and then reads the child tables that refer to the parent table with a foreign key.
The origin uses this table order only for the initial reading of the tables. When threads switch back to previously read tables, they read from the next available table, regardless of the defined order.
Processing Queue
The Oracle Multitable Consumer origin maintains a virtual queue to determine the data to process from different tables. The queue includes each table defined in the origin. When a table is to be processed by partition, multiple partitions for the table are added to the queue, limited by the Max Partitions property defined for each table configuration on the Tables tab.
The origin rotates and reorganizes the queue based on the Per Batch Strategy property. The origin processes data from the queue with the threads specified in the Number of Threads property and the Batches from Result Set property.
Below are some scenarios to show how the queue works.
Multithreaded Table Processing Only
- Multithreaded Partition Processing Mode property is set to Off.
- Multithreaded Partition Processing Mode property is set to On (Best Effort) and no tables meet the partition processing requirements.
A B C D
When
a thread becomes available, it processes data from the first table in the queue. The
processing of the tables depends on how you define the Per Batch Strategy property:- Process All Available Rows in the Table
- With this batch strategy, threads process all data in a table before proceeding to the next table.
- Switch Tables
- With this batch strategy, each thread processes a single batch from a table before moving to the next available table, so the threads cycle through the tables quickly.
Multithreaded Partition Processing Only
- Multithreaded Partition Processing Mode property is set to On (Required).
- Multithreaded Partition Processing Mode property is set to On (Best Effort) and all tables meet the partition processing requirements.
Say you have tables A, B, and C, and all three tables have a lot of data to process. Tables A and B are configured with a maximum of 3 active partitions. And since table C has the largest volume of data, you allow an unlimited number of partitions, which means double the number of threads for the pipeline. Again, let's use the alphabetical initial table ordering.
- Process All Available Rows in the Table
- As when processing tables, when processing partitions with this batch
strategy, threads process all data in a partition before proceeding to the
next partition. Note: In practice, this means that rows from subsequent tables can be processed before a previous table is completed, since available threads continue to pick up partitions from the queue.
- Switch Tables
- As when processing tables, when processing partitions with this batch strategy, each thread processes a single batch from a partition before moving to the next available partition, so the threads cycle through the partitions quickly.
Both Multithreaded Partition and Table Processing
- Multithreaded Partition Processing Mode property is set to On (Best Effort) and some tables meet the partition processing requirements while other tables do not.
When processing a mix of full tables and partitioned tables, the queue basically behaves the same as when processing only partitions, with full tables being processed as a single partition. Let's walk through it....
A B1 B2 B3 C1 C2 C3 C4 C5 C6
When a thread becomes
available, it processes the first table or partition in the queue. The order of the
queue depends on how you define the Per Batch Strategy, as follows:- Process All Available Rows in the Table
- When processing tables and partitions with this batch strategy, threads
process all data in a table or partition before proceeding to the next
partition. An unpartitioned table, like table A, is processed like a table
with a single partition.
When the pipeline starts, the 3 threads process a set of batches from table A and from partitions B1 and B2, leaving the queue like this:
B3 C1 C2 C3 C4 C5 C6
As each thread completes processing, it picks up the next table or partition at the front of the queue. After each of the 3 threads takes another table or partition, the queue looks like this:C3 C4 C5 C6
- Switch Tables
- When processing tables and partitions with this batch strategy, each thread processes a single batch from a table or partition before moving to the next available table or partition, so the threads cycle through the queue quickly.
Generated Records
The Oracle Multitable Consumer origin generates record header attributes and field attributes that provide additional information about each record and field.
CRUD Operation Header Attributes
- sdc.operation.type
- The origin evaluates the operation type associated
with each entry that it processes. When appropriate, it writes the
operation type to the
sdc.operation.type
record header attribute. - oracle.cdc.operation
- The origin also writes the CRUD operation type to an
oracle.cdc.operation
attribute. This attribute was implemented in an earlier release and is supported for backward compatibility.
JDBC Header Attributes
By default, the Oracle Multitable Consumer origin generates JDBC record header attributes that provide additional information about each record, such as the original data type of a field or the source tables for the record.
You can use the record:attribute
or
record:attributeOrDefault
functions to access the information
in the attributes. For more information about working with record header attributes,
see Working with Header Attributes.
JDBC record header attributes include a jdbc
prefix to differentiate
the JDBC attributes from other record header attributes.
You can configure the origin to skip creating JDBC header attributes by clearing the Create JDBC Header Attributes property on the Advanced tab.
JDBC Header Attribute | Description |
---|---|
jdbc.tables |
Provides a
comma-separated list of source tables for the fields in the
record.
Note: Not all JDBC drivers
provide this information. |
jdbc.partition | Provides the full offset key for the partition that produced the record. |
jdbc.threadNumber | Provides the number of the thread that produced the record. |
jdbc.vendor | Provides the name of the database vendor. |
jdbc.<column name>.jdbcType | Provides the numeric value of the original SQL data type for each field in the record. See the Java documentation for a list of the data types that correspond to numeric values. |
jdbc.<column name>.precision | Provides the original precision for all numeric and decimal fields. |
jdbc.<column name>.scale | Provides the original scale for all numeric and decimal fields. |
jdbc.primaryKeySpecification | Provides a JSON-formatted
string that lists the columns that form the primary key in the table and the
metadata for those columns. For example, a table with a
composite primary key contains the following attribute:
A table without a primary key
contains the attribute with an empty value:
|
JDBC Field Attributes
The Oracle Multitable Consumer origin generates field attributes for columns converted to the Decimal or Datetime data types in Data Collector. The attributes provide additional information about each field.
- Decimal and Numeric data types are converted to the Data Collector Decimal data type, which does not store scale and precision.
- The Timestamp data type is converted to the Data Collector Datetime data type, which does not store nanoseconds.
Data Collector Data Type | Generated Field Attribute | Description |
---|---|---|
Decimal | precision | Provides the original precision for every decimal or numeric column. |
Decimal | scale | Provides the original scale for every decimal or numeric column. |
Datetime | nanoSeconds | Provides the original nanoseconds for every timestamp column. |
You can use the record:fieldAttribute
or
record:fieldAttributeOrDefault
functions to access the information
in the attributes. For more information about working with field attributes, see Field Attributes.
Event Generation
The Oracle Multitable Consumer origin can generate events that you can use in an event stream. When you enable event generation, the origin generates an event when it completes processing the data returned by the specified queries for all tables. It also generates events when it completes processing the data returned from a table and the data returned from a schema.
- With the Pipeline Finisher executor to
stop the pipeline and transition the pipeline to a Finished state when
the origin completes processing available data.
When you restart a pipeline stopped by the Pipeline Finisher executor, the origin continues processing from the last-saved offset unless you reset the origin.
For an example, see Stopping a Pipeline After Processing All Available Data.
- With the Email executor to send a custom email
after receiving an event.
For an example, see Sending Email During Pipeline Processing.
-
With a destination to store information about completed queries.
For an example, see Preserving an Audit Trail of Events.
For more information about dataflow triggers and the event framework, see Dataflow Triggers Overview.
Event Records
Record Header Attribute | Description |
---|---|
sdc.event.type | Event type. Uses the following type:
|
sdc.event.version | Integer that indicates the version of the event record type. |
sdc.event.creation_timestamp | Epoch timestamp when the stage created the event. |
The Oracle Multitable Consumer origin can generate the following event records:
- no-more-data
- The Oracle Multitable Consumer origin generates a no-more-data event record when the origin completes processing all data returned by the queries for all tables.
- schema-finished
- The Oracle Multitable Consumer origin generates a schema-finished event record when the origin completes processing all data within a schema.
- table-finished
- The Oracle Multitable Consumer origin generates a table-finished event record when the origin completes processing all data within a table.
Configuring an Oracle Multitable Consumer Origin
Configure an Oracle Multitable Consumer origin to read Oracle database data from multiple tables.
-
In the Properties panel, on the General tab, configure the
following properties:
General Property Description Name Stage name. Description Optional description. Produce Events Generates event records when events occur. Use for event handling. On Record Error Error record handling for the stage: - Discard - Discards the record.
- Send to Error - Sends the record to the pipeline for error handling.
- Stop Pipeline - Stops the pipeline.
-
On the JDBC tab, configure the following properties:
JDBC Property Description Connection Connection that defines the information required to connect to an external system. To connect to an external system, you can select a connection that contains the details, or you can directly enter the details in the pipeline. When you select a connection, Control Hub hides other properties so that you cannot directly enter connection details in the pipeline.
Hostname Host name of the database. Port Port number to use. Service Name Listener-registered service name. Use Connection String Use a JDBC connection string to connect to the Oracle database. Oracle JDBC Connection String Connection string used to connect to the database. Enter the connection string in the following format:
jdbc:oracle:<driver_type>:@<host>:<port>:<service_name>
You can optionally include the user name and password in the connection string.
Required when Use Connection String is enabled.
Use Credentials Enables entering credentials on the Credentials tab. Select when you do not include credentials in the JDBC connection string. Queries per Second Maximum number of queries to run in a second across all partitions and tables. Use 0 for no limit. Default is 10.
Number of Threads Number of threads the origin generates and uses for multithreaded processing. Configure the Maximum Pool Size property on the Advanced tab to be equal to or greater than this value.
Per Batch Strategy Strategy to create each batch of data:- Switch Tables - When performing only multithreaded table processing, each thread creates a batch of data from one table, and then switches to the next available table to create the next batch. Define the Batches from Result Set property when you configure a Switch Tables strategy.
- Process All Available Rows From the Table - When performing only multithreaded table processing, each thread processes all data in a table before moving to the next table.
When performing multithreaded partition processing or a mix of table and partition processing, the behavior for each batch strategy is more complicated. For details, see Batch Strategy or Processing Queue.
Max Batch Size (records) Maximum number of records to include in a batch. Maximum Number of Tables Maximum number of tables to prefetch. This limit is checked as the pipeline starts. If it is exceeded, the origin generates an error and stops the pipeline.
Default is 5. Use -1 to allow an unlimited number of tables to be prefetched.
Available when using the Switch Tables batch strategy.
Batches from Result Set Maximum number of batches to create from a result set. After a thread creates this number of batches from a result set, the result set closes. Then, any available thread can read from the table or partition. Use a positive integer to set a limit on the number of batches created from the result set. Use -1 to create an unlimited number of batches from a result set.
By default, the origin creates an unlimited number of batches from the result set, keeping the result set open as long as possible.
Available when using the Switch Tables batch strategy.
Result Set Cache Size Number of result sets to cache in the database. Use a positive integer to set a limit on the number of cached result sets. Use -1 to opt out of this property. By default, the origin caches an unlimited number of result sets.
Max Clob Size (characters) Maximum number of characters to be read in a Clob field. Larger data is truncated. Max Blob Size (bytes) Maximum number of bytes to be read in a Blob field.
Number of Retries on SQL Error Number of times a thread tries to read a batch of data after receiving an SQL error. After a thread retries this number of times, the thread handles the error based on the error handling configured for the origin. Use to handle transient network or connection issues that prevent a thread from reading a batch of data.
Default is 0.
Use Quoted Identifiers Encloses table and column names in quotation marks.
When this property is disabled, the destination uses the standard naming conventions for Oracle.
Data Time Zone Time zone to use to evaluate datetime-based offset column conditions. No-more-data Event Generation Delay (seconds) Number of seconds to delay generation of the no-more-data event after processing all rows. Use to allow time for additional data to arrive before generating the no-more-data event. Convert Timestamp To String Enables the origin to write timestamps as string values rather than datetime values. Strings maintain the precision stored in the source system.
When writing timestamps to Data Collector date or time data types that do not store nanoseconds, the origin stores any nanoseconds from the timestamp in a field attribute.
Fetch Size Maximum number of rows to fetch and store in memory on the Data Collector machine. The size cannot be zero. Default is 1,000.
For more information about configuring a fetch size, see your database documentation.
Query Timeout (seconds) Maximum number of seconds to wait for a query to complete. Use 0 for no query timeout.
Additional JDBC Configuration Properties Additional JDBC configuration properties to use. To add properties, click Add and define the JDBC property name and value. Use the property names and values as expected by JDBC.
-
On the Tables tab, define one or more table
configurations. Using simple or bulk edit mode, click the Add icon
to define another table configuration.
Configure the following properties for each table configuration:
Tables Property Description Schema Pattern of the schema names included in this table configuration. Use the SQL LIKE syntax to define the pattern. Enter % to match all schemas. If you enter no value, the origin only reads from tables without a specified schema. Note: Oracle uses all caps for schema, table, and column names by default. Names can be lower- or mixed-case only if the schema, table, or column was created with quotation marks around the name.Enable Table Name List Enables you to specify a list of multiple table name patterns to read for this table configuration instead of being limited to a single table name pattern. Table Name Pattern List Pattern of the table names to read for this table configuration. Use the SQL LIKE syntax to define the pattern. Click Add Another to add additional table name patterns. Note: Oracle uses all caps for schema, table, and column names by default. Names can be lower- or mixed-case only if the schema, table, or column was created with quotation marks around the name.Available when table name list is enabled.
Table Name Pattern Pattern of the table names to read for this table configuration. Use the SQL LIKE syntax to define the pattern. Note: Oracle uses all caps for schema, table, and column names by default. Names can be lower- or mixed-case only if the schema, table, or column was created with quotation marks around the name.Default is the percentage wildcard (%) which matches all tables in the schema.
Available when table name list is disabled.
Table Exclusion Pattern Pattern of the table names to exclude from being read for this table configuration. Use a Java-based regular expression, or regex, to define the pattern. Leave empty if you do not need to exclude any tables.
Schema Exclusion Pattern Pattern of the schema names to exclude from being read for this table configuration. Use a Java-based regular expression, or regex, to define the pattern. Leave empty if you do not need to exclude any schemas.
Override Offset Columns Determines whether to use the primary keys or other columns as the offset columns for this table configuration. Select to override the primary keys and define other offset columns. Clear to use existing primary keys as the offset columns.
To perform multithreaded partition processing on a table with multiple key columns or a key column with unsupported data types, select this option and specify a valid offset column. For more information about partition processing requirements, see Partition Processing Requirements.
Offset Columns Offset columns to use. As a best practice, an offset column should be an incremental and unique column that does not contain null values. Having an index on this column is strongly encouraged since the underlying query uses an ORDER BY and inequality operators on this column.
Initial Offset Offset value to use for this table configuration when the pipeline starts. Enter the primary key name or offset column name and value. For Datetime columns, enter a Long value. When you define multiple offset columns, you must define an initial offset value for each column, in the same order that the columns are defined.
Last Offset Offset value for this table configuration where the origin stops reading. Enter the primary key name or offset column name and value. For Datetime columns, enter a Long value. When you define multiple offset columns, you must define an initial last value for each column, in the same order that the columns are defined.
Note: The last offset is only applicable in cases where the Partition Processing Requirements under Multithreaded Partition Processing are met.Enable Non-Incremental Load Enables non-incremental processing of tables that do not include a primary key or offset column. Do not use when requiring multithreaded partition processing. Multithreaded Partition Processing Mode Determines how the origin performs multithreaded processing. Select one of the following options: - Off - The origin performs multithreaded table processing.
Can be used to perform non-incremental processing of tables without key or offset columns.
- On (Best Effort) - The origin performs multithreaded partition processing for
all tables that meet the partition processing requirements and
performs multithreaded table partitioning tables
with multiple key or offset columns.
Can be used to perform non-incremental processing of tables without key or offset columns.
- On (Required) - The origin performs multithreaded
partition processing for all tables.
Generates an error if the table configuration includes tables that do not meet the partition processing requirements.
Partition Size Range of values in the offset column to use to create partitions. If the offset column is a Datetime column, provide the partition size in milliseconds. For example, to create a partition for every hour, enter 3,600,000.
Available when using multithreaded partition processing.
Max Partitions Maximum number of partitions to be maintained or processed at one time for a single table. Adjusting this value can increase throughput depending on various factors, including the machine running Data Collector and the database server type and capacity. The minimum positive value is 2, to ensure the origin can make progress through the partitions.
Enter -1 to use the default behavior, allowing the origin to create up to twice as many partitions for each table as threads used by the origin. Best practice is to start with the default behavior and adjust to tune performance.
Available when using multithreaded partition processing.
Offset Column Conditions Additional conditions that the origin uses to determine where to start reading data for this table configuration. The origin adds the defined condition to the WHERE clause of the SQL query. Use the expression language to define the conditions. For example, you can use the offset:column function to compare the value of an offset column.
- Off - The origin performs multithreaded table processing.
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If you configured the destination to use credentials
separately from the Oracle JDBC connection string on the
JDBC tab, then configure the following properties on
the Credentials tab:
Credentials Property Description Username User name for the JDBC connection. The user account must have the correct permissions or privileges in the database.
Password Password for the JDBC user name. Tip: To secure sensitive information such as user names and passwords, you can use runtime resources or credential stores. For more information about credential stores, see Credential Stores in the Data Collector documentation. -
When using JDBC versions older than 4.0, on the Legacy
Drivers tab, optionally configure the following
properties:
Legacy Drivers Property Description JDBC Class Driver Name Class name for the JDBC driver. Required for JDBC versions older than version 4.0. Connection Health Test Query Optional query to test the health of a connection. Recommended only when the JDBC version is older than 4.0. -
On the Advanced tab, optionally configure advanced
properties.
The defaults for these properties should work in most cases:
Advanced Property Description Maximum Pool Size Maximum number of connections to create. Must be equal to or greater than the value of the Number of Threads property. Default is 1.
Minimum Idle Connections Minimum number of connections to create and maintain. To define a fixed connection pool, set to the same value as Maximum Pool Size. Default is 1.
Connection Timeout (seconds) Maximum time to wait for a connection. Use a time constant in an expression to define the time increment. Default is 30 seconds, defined as follows:${30 * SECONDS}
Idle Timeout (seconds) Maximum time to allow a connection to idle. Use a time constant in an expression to define the time increment. Use 0 to avoid removing any idle connections.
When the entered value is close to or more than the maximum lifetime for a connection, Data Collector ignores the idle timeout.
Default is 10 minutes, defined as follows:${10 * MINUTES}
Max Connection Lifetime (seconds) Maximum lifetime for a connection. Use a time constant in an expression to define the time increment. Use 0 to set no maximum lifetime.
When a maximum lifetime is set, the minimum valid value is 30 minutes.
Default is 30 minutes, defined as follows:${30 * MINUTES}
Auto Commit Determines if auto-commit mode is enabled. In auto-commit mode, the database commits the data for each record. Default is disabled.
Enforce Read-only Connection Creates read-only connections to avoid any type of write. Default is enabled. Disabling this property is not recommended.
Transaction Isolation Transaction isolation level used to connect to the database. Default is the default transaction isolation level set for the database. You can override the database default by setting the level to any of the following:
- Read committed
- Read uncommitted
- Repeatable read
- Serializable
Init Query SQL query to perform immediately after the stage connects to the database. Use to set up the database session as needed. The query is performed after each connection to the database. If the stage disconnects from the database during the pipeline run, for example if a network timeout occurrs, the stage performs the query again when it reconnects to the database.
For example, in case of Oracle, the following query returns 1 to verify that the stage is connected to the database:
Select 1 from dual;
Initial Table Order Strategy Initial order used to read the tables: - None - Reads the tables in the order that they are listed in the database.
- Alphabetical - Reads the tables in alphabetical order.
- Referential Constraints - Reads the tables based on the dependencies between the tables.
Create JDBC Header Attributes Adds JDBC header attributes to records. The origin creates JDBC header attributes by default. Note: When using the origin with a Drift Synchronization Solution, make sure this property is selected.On Unknown Type Action to take when encountering an unsupported data type: - Stop Pipeline - Stops the pipeline after completing the processing of the previous records.
- Convert to String - When possible, converts the data to string and continues processing.
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On the Data tab, configure the following properties:
Data Property Description Column Fields to Ignore A list of fields to ignore when writing to the destination. You can enter a comma-separated list of first-level fields to ignore. Ignore Fields with Invalid Type Allows replacing fields that contain data of an invalid type with an empty value. When not enabled, records with data of invalid types are treated as error records.
Merge CDC Data Enables performing CRUD operations and using the MERGE command to write to Oracle tables. Select to process CDC data. Important: To maintain the original order of data, do not use multiple threads or cluster execution mode when processing CDC data. -
On the SSL/TLS Encryption tab, configure the following
properties:
SSL/TLS Property Description Encrypt Connection Encrypt the connection using SSL. Server Certificate PEM Server certificate in PEM format used to verify the SSL/TLS certificate of the Oracle server. Use a text editor to open the PEM encoded certificate, and then copy and paste the full contents of the file into the property, including the header and footer.
Cipher Suites Cipher suites to use. To use a cipher suite that is not a part of the default set, click the Add icon and enter the name of the cipher suite. You can use simple or bulk edit mode to add cipher suites. Enter the Java Secure Socket Extension (JSSE) name for the additional cipher suites that you want to use.
Verify Hostname Verify that the host name specified in the connection string matches the certificate name in the server certificate. SSL Distinguished Name Distinguished Name (DN) of the server. This must match the DN in the server certificate.