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Performance kiss of death factors in transactional replication
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Although these performance monitor counters are the best way to get a handle on your current throughput and latency in your production environments, in this study we ll be focusing primarily on throughput. We ll focus mainly on worker time, or how long the distribution agent has to work to replicate a given set of commands. We ll focus on the Distribution Agent metrics, as the Log Reader is rarely the bottleneck in a replication topology. Additionally the Log Reader Agent operates asynchronously from the Distribution Agent; therefore, the Log Reader Agent can keep current with reading the log, while the Distribution Agent can be experiencing high latencies. By studying the output of the replication agents themselves, when you replay your workloads through them (or measure your workloads as they are replicated by the agents), you can determine the optimal configuration of profile settings for your workloads, and determine how to group articles into different publications for the maximum throughput. This chapter assumes that you have a good understanding of replication concepts. Should you be unfamiliar with replication concepts, I advise you to study the section Replication Administrator InfoCenter in Books Online, accessible online at http:/ / msdn.microsoft.com/en-us/library/ms151314(SQL.90).aspx. Before we begin it is important to look at factors that are the performance kiss of death to any replication solution. After we look at these factors and possible ways to mitigate them, we ll look at tuning the replication agents themselves for maximum performance.
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Performance kiss of death factors in transactional replication
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The following factors will adversely affect the throughput of any replication solution: Batch updates Replicating text Logging Network latency Subscriber hardware Subscriber indexes and triggers Distributor hardware Large numbers of push subscriptions We ll look at each of these in turn.
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Transactional replication replicates transactions within a transactional context hence the name transactional. This means that if I do a batch update, insert, or delete, the batch is written in the log as singleton commands. Singletons are data manipulation language (DML) commands that affect at most one row. For example, the following are all singletons:
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High-performance transactional replication
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insert into tableName (Col1, Col2) values(1,2) update tableName set Col1=1, Col2=2 where pk=1 delete from tableName where pk=1
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Each singleton is wrapped in a transaction. Contrast this with the following batch updates (the term update refers to any DML an insert, update, or delete:
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insert into tableName select * from tableName1 update tableName set col1=1 where pk<=20 delete from tableName where pk<=20
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In the insert statement the insert batch update will insert as many rows as there are in tableName1 into tableName (as a transaction). Assuming there were 20 rows with a pk less than or equal to 20 in tableName, 20 rows would be affected by the batch update and batch deletes. If you use any transaction log analysis tool, you ll see that the batch updates are decomposed into singleton commands. The following update command
update tableName set col1=1 where pk<=20
would be written in the log as 20 singleton commands, that is:
update update update ... update tableName set col1=1 where pk=1 tableName set col1=1 where pk=2 tableName set col1=1 where pk=3 tableName set col1=1 where pk=20
The Log Reader Agent reads committed transactions and their constituent singleton commands in the log and writes them to the distribution database as the constituent commands. Details about the transaction are written to MSrepl_transactions along with details about the constituent commands. The Distribution Agent wakes up (if scheduled) or polls (if running continuously) and reads the last applied transaction on the subscription database for that publication. It then reads MSrepl_transactions on the distribution database and applies the corresponding commands for that transaction it finds in MSrepl_commands one by one on the Subscriber. Transactions are committed to the database depending on the settings of the BatchCommitSize and BatchCommitThreshold settings for the Distribution Agent. We ll talk about these settings later. Key to understanding the performance impact of this architecture is realizing that replicating large transactions means that a transaction will be held on the Subscriber while all the singleton commands are being applied on the Subscriber. Then a commit is issued. This allows the Distribution Agent to roll back the entire transaction, should there be a primary key violation, foreign key violation, lack of transactional consistency (no rows affected), or some other event that causes the DML to fail (for
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