RTL groups together as many compilable transactions as possible, compiles the transactions in the group into a net change, then uses the bulk interface in the replicate database to apply the net change to the replicate database.
When replicating into Sybase IQ replicate databases with identical database schema, RTL uses:
Compilation – rearranges replicate data by each table, and each insert, update, and delete operation, and compiling the operations into net-row operations.
Bulk apply – applies the net result of the compilation operations in bulk using the most efficient bulk interface for the net result. Replication Server uses an in-memory net-change database to store the net row changes that are then applied to the replicate database.
RTL improves performance for replication to Sybase IQ compared to the continuous replication mode and a staging solution for example, by using:
Reduced number of external components – reduced maintenance costs and overhead, since there is no requirement for the staging database.
Reduced latency – no overhead from the staging solution and with replication directly into Sybase IQ.
Improved usability – the RTL configuration does not require any of: function-string mapping, DSI suspend and resume, data population from staging database to Sybase IQ, scheduling activities for the staging solution.
Compilation and bulk apply – instead of sending every logged operation, RTL compilation removes the intermediate insert, update, or delete operations in a group of operations and sends only the final compiled state of a replicated transaction. Depending on the transaction profile, this generally means that Replication Server sends a smaller number of commands to Sybase IQ to process.
Sybase IQ provides a bulk interface that improves insert operation performance compared with the SQL language mode operation. RTL takes advantage of the Sybase IQ bulk interface to improve performance for insert as well as update and delete operations.
As Replication Server compiles and combines a larger number of transactions into a group, bulk operation processing improves; therefore, replication throughput and performance also improves. You can adjust group sizes to control the amount of data that is grouped together for bulk apply.