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Configuring replication methods

Replication methods and rechecks are used to identify new or updated data in your database tables. Setting them correctly is crucial to ensuring both data accuracy and optimized update times. This topic focuses on replication methods.

When new tables are synced in the Data Warehouse Manager, a replication method is automatically chosen for the table. Understanding the various replication methods, how tables are organized, and how the table data behaves allows you to choose the best replication method for your tables.

What are the replication methods?

Replication methods fall into three groups - Incremental, Full Table, and Paused.

Incremental Replication means that Commerce Intelligence replicates only new or updated data on every replication attempt. As these methods greatly reduce latency, ÃÛ¶¹ÊÓƵ recommends using it where possible.

Full Table Replication means that Commerce Intelligence replicates the entire contents of a table on every replication attempt. Because of the potentially large amount of data to be replicated, these methods can increase latency and update times. If a table contains any timestamped or datetime columns, ÃÛ¶¹ÊÓƵ recommends using an Incremental method instead.

Paused indicates that replication for the table is stopped or paused. Commerce Intelligence does not check for new or updated data during an update cycle; this means that no data is replicated from a table that has this as its Replication Method.

Incremental replication methods incremental

Modified At (most ideal)

The Modified At replication method uses a datetime column - which is populated when a row is created and then updated when data changes - to find data to replicate. This method is designed to work with tables that meet the following criteria:

In addition to those criteria, ÃÛ¶¹ÊÓƵ recommends indexing the datetime column used for Modified At replication, as this helps optimize replication speed.

When the update runs, new or changed data is identified by searching for rows that have a value in the datetime column that occurred after the most recent update. When new rows are discovered, they are replicated to your Data Warehouse. If any rows exist in the Data Warehouse Manager, they are overwritten with the current database values.

For example, a table may have a column called modified\_at that indicates the last time data was changed. If the most recent update ran Tuesday at noon, the update searches for all rows having a modified\_at value greater than Tuesday at noon. Any discovered rows that were either created or modified since noon on Tuesday are replicated to the Data Warehouse.

Did you know?
Even if your database cannot currently support an Incremental Replication method, you may be able to make changes to your database that would enable use of Modified At or Single Auto Incrementing PK.

Modified At is not only the most ideal replication method, it is also the fastest. This method not only produces noticeable speed increases with large data sets, it also does not require configuring a recheck option. Other methods need to iterate through an entire table to identify changes, even if a small subset of data has changed. Modified At iterates through only that small subset.

Single Auto Incrementing Primary Key

Auto Incrementing is a behavior that sequentially assigns primary keys to rows. If a table is Auto Incrementing and the highest primary key in the table is 1,000, then the next primary value is 1,001 or higher. A table that does not use Auto Incrementing behavior may assign a primary key value that is less than 1,000 or jump to a much bigger number, but this is not commonly used.

This method is designed to replicate new data from tables that meet the following criteria:

When a table is using Single Auto Incrementing Primary Key replication, new data is discovered by searching for primary key values that are higher than the current highest value in your Data Warehouse. For example, if the highest primary key value in your Data Warehouse is 500, when the next update runs it will search for rows with primary key values of 501 or higher.

Add Date

The Add Date method functions similarly to the Single Auto Incrementing Primary Key method. Instead of using an integer for the table’s primary key, this method uses a timestamped column to check for new rows.

When a table uses Add Date replication, new data is discovered by searching for timestamped values that are greater than the latest date synced to your Data Warehouse. For example, if an update last ran on 20/12/2015 09:00:00, any rows with a timestamp greater than this will be marked as new data and replicated.

Full table replication methods fulltable

Full Table

Full table replication refreshes the entire table any time new rows are detected. This is by far the least efficient replication method, because all data must be reprocessed during every update, assuming there are new rows.

New rows are detected by querying your database at the start of the synchronization process and counting the number of rows. If your local database contains more rows than Commerce Intelligence, then the table is refreshed. If the row counts are identical, or if Commerce Intelligence contains more rows than your local database, then the table is skipped.

This raises the important point that Full Table replication is incompatible when:

In either of the above scenarios, Full Table replication does not detect any changes and your data becomes stale. Due to the inefficiency of this replication method, and the requirements mentioned above, Full Table replication is only recommended as a last resort.

Primary Key Batch

When a table uses Primary Key Batch (PK Batch), new data is discovered by counting rows inside ranges, or batches, of primary key values. While you typically think of this being used with integers, even text values can be ordered in a way that allows the system to define constant ranges.

For example, say that an update runs and performs a row count for the range of keys from 1 to 100. In this update, the system finds and logs 37 rows. In the next update, a row count is performed again on the 1-100 range and finds 41 rows. Because there is a difference in the number of rows compared to the last update, the system inspects that range (or batch) in more detail.

This method is intended to replicate data from tables that meet the following criteria:

This method is not ideal, as it is incredibly slow due to the amount of processing that must occur to examine batches and find changes. ÃÛ¶¹ÊÓƵ recommends not using this method unless it is impossible to make necessary modifications to support the other replication methods. Expect update times to increase if this method must be used.

Setting replication methods

Replication methods are set on a table-by-table basis. To set a replication method for a table, you need Admin permissions so you can access the Data Warehouse Manager.

Look at the whole process:

Wrapping up

To finish up, you have put together this table that compares the various replication methods. It is incredibly handy when selecting a method for the tables in your Data Warehouse.

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