Amazon Redshift Federated Query enables you to use the analytic power of Amazon Redshift to directly query data stored in Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL databases. For more information about setting up an environment where you can try out Federated Query, see Accelerate Amazon Redshift Federated Query adoption with AWS CloudFormation.
Federated Query enables real-time data integration and simplified ETL processing. You can now connect live data sources directly in Amazon Redshift to provide real-time reporting and analysis. Previously, you needed to extract data from your PostgreSQL database to Amazon Simple Storage Service (Amazon S3) and load it to Amazon Redshift using
COPY or query it from Amazon S3 with Amazon Redshift Spectrum. For more information about the benefits of Federated Query, see Build a Simplified ETL and Live Data Query Solution using Amazon Redshift Federated Query.
This post discusses 10 best practices to help you maximize the benefits of Federated Query when you have large federated data sets, when your federated queries retrieve large volumes of data, or when you have many Redshift users accessing federated data sets. These techniques are not necessary for general usage of Federated Query. They are intended for advanced users who want to make the most of this exciting feature.
The best practices are divided into two sections: the first for advice that applies to your Amazon Redshift cluster, and the second for advice that applies to your Aurora PostgreSQL and Amazon RDS for PostgreSQL environments.
The code examples provided in this post derive from the data and queries in the CloudDataWarehouseBenchmark GitHub repo (based on TPC-H and TPC-DS).
Best practices to apply in Amazon Redshift
The following best practices apply to your Amazon Redshift cluster when using federated queries to access your Aurora or Amazon RDS for PostgreSQL instances.
1. Use separate external schemas for each use case
Consider creating separate Amazon Redshift external schemas, using separate remote PostgreSQL users, for each specific Amazon Redshift use case. This practice allows you to have extra control over the users and groups who can access the external database. For instance, you may want to have an external schema for ETL usage, with an associated PostgreSQL user, that has broad access and another schema, and an associated PostgreSQL user for ad-hoc reporting and analysis with access limited to specific resources.
The following code example creates two external schemas for ETL use and ad-hoc reporting use. Each schema uses a different
SECRET_ARN containing credentials for separate users in the PostgreSQL database.
2. Use query timeouts to limit total runtimes
Consider setting a timeout on the users or groups that have access to your external schemas. User queries could unintentionally try to retrieve a very large number of rows from the external relation and remain running for an extended time, which holds open resources in both Amazon Redshift and PostgreSQL.
To limit the total runtime of a user’s queries, you can set a
statement_timeout for all a user’s queries. The following code example sets a 2-hour timeout for an ETL user:
If many users have access to your external schemas, it may not be practical to define a
statement_timeout for each individual user. Instead, you can add a query monitoring rule in your WLM configuration using the
query_execution_time metric. The following screenshot shows an Auto WLM configuration with an Adhoc Reporting queue for users in the
adhoc group, with a rule that cancels queries that run for longer than 1,800 seconds (30 minutes).
3. Make sure the Amazon Redshift query plan is efficient
Review the overall query plan and query metrics of your federated queries to make sure that Amazon Redshift processes them efficiently. For more information about query plans, see Evaluating the query plan.
Viewing the Amazon Redshift query explain plan
You can retrieve the plan for your query by prefixing your SQL with
EXPLAIN and running that in your SQL client. The following code example is the explain output for a sample query:
XN PG Query Scan indicates that Amazon Redshift will run a query against the federated PostgreSQL database for this part of the query, we refer to this as the “federated subquery” in this post. When your query uses multiple federated data sources Amazon Redshift runs a federated subquery for each source. Amazon Redshift runs each federated subquery from a randomly selected node in the cluster.
XN PG Query Scan line, you can see
Remote PG Seq Scan followed by a line with a
Filter: element. These two lines define how Amazon Redshift accesses the external data and the predicate used in the federated subquery. You can see that the federated subquery will run against the federated table
You can also see from
rows=19999460 that Amazon Redshift estimates that the query can return up to 20 million rows from PostgreSQL. It creates this estimate by asking PostgreSQL for statistics about the table.
Since each federated subquery runs from a single node in the cluster, Amazon Redshift must choose a join distribution strategy to send the rows returned from the federated subquery to the rest of the cluster to complete the joins in your query. The choice of a broadcast or distribution strategy is indicated in the explain plan. Operators that start with
DS_BCAST broadcast a full copy of the data to all nodes. Operators that start with
DS_DIST distribute a portion of the data to each node in the cluster.
It’s usually most efficient to broadcast small results and distribute larger results. When the planner has a good estimate of the number of rows that the federated subquery will return, it chooses the correct join distribution strategy. However, if the planner’s estimate isn’t accurate, it may choose broadcast for result that is too large, which can slow down your query.
Joins should use the smaller result as the inner relation. When your query joins two tables (or two federated subqueries), Amazon Redshift must choose how best to perform the join. The query planner may not perform joins in the order declared in your query. Instead, it uses the information it has about the relations being joined to create estimated costs for a variety of possible plans. It uses the plan, including join order, that has the lowest expected cost.
When you use a hash join, the most common join, Amazon Redshift constructs a hash table from the inner table (or result) and compares it to every row from the outer table. You want to use the smallest result as the inner so that the hash table can fit in memory. The chosen ordering join may not be optimal if the planner’s estimate doesn’t reflect the real size of the results from each step in the query.
Improving query efficiency
The following is high-level advice for improving efficiency. For more information, see Analyzing the query plan.
- Examine the plan for separate parts of your query. If your query has multiple joins or uses subqueries, you can review the explain plan for each join or subquery to check whether the query benefits from being simplified. For instance, if you use several joins, examine the plan for a simpler query using only one join to see how Amazon Redshift plans that join on its own.
- Examine the order of outer joins and use an inner join. The planner can’t always reorder outer joins. If you can convert an outer join to an inner join, it may allow the planner to use a more efficient plan.
- Reference the distribution key of the largest Amazon Redshift table in the join. When a join references the distribution key Amazon Redshift can complete the join on each node in parallel without moving the rows from the Redshift table across the cluster.
- Insert the federated subquery result into a table. Amazon Redshift has optimal statistics when the data comes from a local temporary or permanent table. In rare cases, it may be most efficient to store the federated data in a temporary table first and join it with your Amazon Redshift data.
4. Make sure predicates are pushed down to the remote query
Amazon Redshift’s query optimizer is very effective at pushing predicate conditions down to the federated subquery that runs in PostgreSQL. Review the query plan of important or long-running federated queries to check that Amazon Redshift applies all applicable predicates to each subquery.
Consider the following example query, in which the predicate is inside a
CASE statement and the federated relation is within a CTE subquery:
Amazon Redshift can still effectively optimize the federated subquery by pushing a filter down to the remote relation. See the following plan:
If Redshift can’t push your predicates down as needed, or the query still returns too much data, consider the advice in the following two sections regarding materialized views and syncing tables. To easily rewrite your queries to achieve effective filter pushdown, consider the advice in the final best practice regarding persisting frequently queried data.
5. Use materialized views to cache frequently accessed data
Amazon Redshift now supports the creation of materialized views that reference federated tables in external schemas.
Cache queries that run often
Consider caching frequently run queries in your Amazon Redshift cluster using a materialized view. When many users run the same federated query regularly, the remote content of the query must be retrieved again for each execution. With a materialized view, the results can instead be retrieved from your Amazon Redshift cluster without getting the same data from the remote database. You can then schedule the refresh of the materialized view to happen at a specific time, depending upon the change rate and importance of the remote data.
The following code example demonstrates the creation, querying, and refresh of a materialized view from a query that uses a federated source table:
Cache tables that are used by many queries
Also consider locally caching tables used by many queries using a materialized view. When many different queries use the same federated table it’s often better to create a materialized view for that federated table which can then be referenced by the other queries instead.
The following code example demonstrates the creation and querying of a materialized view on a single federated source table:
As of this writing, you can’t reference a materialized view inside another materialized view. Other views that use the cached table need to be regular views.
Balance caching against refresh time and frequency
The use of materialized views is best suited for queries that run quickly relative to the refresh schedule. For example, a materialized view refreshed hourly should run in a few minutes, and a materialized view refreshed daily should run in less than an hour. As of this writing, materialized views that reference external tables aren’t eligible for incremental refresh. A full refresh occurs when you run
REFRESH MATERIALIZED VIEW and recreate the entire result.
Limit remote access using materialized views
Also consider using materialized views to reduce the number of users who can issue queries directly against your remote databases. You can grant external schema access only to a user who refreshes the materialized views and grant other Amazon Redshift users access only to the materialized view.
Limiting the scope of access in this way is a general best practice for data security when querying from remote production databases that contain sensitive information.
6. Sync large remote tables to a local copy
Consider keeping a copy of the remote table in a permanent Amazon Redshift table. When your remote table is large and a full refresh of a materialized view is time-consuming it’s more effective to use a sync process to keep a local copy updated.
Sync newly added remote data
When your large remote table only has new rows added, not updated nor deleted, you can synchronize your Amazon Redshift copy by periodically inserting the new rows from the remote table into the copy. You can automate this sync process using the example stored procedure
sp_sync_get_new_rows on GitHub.
This example stored procedure requires the source table to have an auto-incrementing identity column as its primary key. It finds the current maximum in your Amazon Redshift table, retrieves all rows in the federated table with a higher ID value, and inserts them into the Amazon Redshift table.
The following code examples demonstrate a sync from a federated source table to a Amazon Redshift target table. First, you create a source table with four rows in the PostgreSQL database:
Create a target table with two rows in your Amazon Redshift cluster:
Call the Amazon Redshift stored procedure to sync the tables:
Merge remote data changes
After you update or insert rows in your remote table, you can synchronize your Amazon Redshift copy by periodically merging the changed rows and new rows from the remote table into the copy. This approach works best when changes are clearly marked in the table so that you can easily retrieve just the new or changed rows. You can automate this sync process using the example stored procedure
sp_sync_merge_changes, on GitHub.
This example stored procedure requires the source to have a date/time column that indicates the last time each row was modified. It uses this column to find changes that you need to sync and either updates the changed rows or inserts new rows in the Amazon Redshift copy. The stored procedure also requires the table to have a primary key declared. It uses the primary key to identify which rows to update in the local copy of the data.
The following code examples demonstrate a refresh from a federated source table to an Amazon Redshift target table. First, create a sample table with two rows in your Amazon Redshift cluster:
Create a source table with four rows in your PostgreSQL database:
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