Using Parallel Query with Amazon Aurora for MySQL


parallel query amazon aurora for mysqlParallel query execution is my favorite, non-existent, feature in MySQL. In all versions of MySQL – at least at the time of writing – when you run a single query it will run in one thread, effectively utilizing one CPU core only. Multiple queries run at the same time will be using different threads and will utilize more than one CPU core.

On multi-core machines – which is the majority of the hardware nowadays – and in the cloud, we have multiple cores available for use. With faster disks (i.e. SSD) we can’t utilize the full potential of IOPS with just one thread.

AWS Aurora (based on MySQL 5.6) now has a version which will support parallelism for SELECT queries (utilizing the read capacity of storage nodes underneath the Aurora cluster). In this article, we will look at how this can improve the reporting/analytical query performance in MySQL. I will compare AWS Aurora with MySQL (Percona Server) 5.6 running on an EC2 instance of the same class.

In Short

Aurora Parallel Query response time (for queries which can not use indexes) can be 5x-10x better compared to the non-parallel fully cached operations. This is a significant improvement for the slow queries.

Test data and versions

For my test, I need to choose:

  1. Aurora instance type and comparison
  2. Dataset
  3. Queries

Aurora instance type and comparison

According to Jeff Barr’s excellent article ( the following instance classes will support parallel query (PQ):

“The instance class determines the number of parallel queries that can be active at a given time:

  • db.r*.large – 1 concurrent parallel query session
  • db.r*.xlarge – 2 concurrent parallel query sessions
  • db.r*.2xlarge – 4 concurrent parallel query sessions
  • db.r*.4xlarge – 8 concurrent parallel query sessions
  • db.r*.8xlarge – 16 concurrent parallel query sessions
  • db.r4.16xlarge – 16 concurrent parallel query sessions”

As I want to maximize the concurrency of parallel query sessions, I have chosen db.r4.8xlarge. For the EC2 instance I will use the same class: r4.8xlarge.


MySQL on ec2


I’m using the “Airlines On-Time Performance” database from  (You can find the scripts I used here:

The table is very wide, 84 columns.

Working with Aurora PQ (Parallel Query)


Aurora PQ works by doing a full table scan (parallel reads are done on the storage level). The InnoDB buffer pool is not used when Parallel Query is utilized.

For the purposes of the test I turned PQ on and off (normally AWS Aurora uses its own heuristics to determine if the PQ will be helpful or not):

Turn on and force:

Turn off:

The EXPLAIN plan in MySQL will also show the details about parallel query execution statistics.


Here, I use the “reporting” queries, running only one query at a time. The queries are similar to those I’ve used in older blog posts comparing MySQL and Apache Spark performance ( )

Here is a summary of the queries:

  1. Simple queries:
    • select count(*) from ontime where flightdate > '2017-01-01'
    • select avg(DepDelay/ArrDelay+1) from ontime
  2. Complex filter, single table:

3. Complex filter, join “reference” table

4. select one row only, no index

Query 1a: simple, count(*)

Let’s take a look at the most simple query: count(*). This variant of the “ontime” table has no secondary indexes.

Aurora, pq (parallel query) disabled:

I disabled the PQ first to compare:

(from the EXPLAIN plan, we can see that parallel query is used).


As we can see the results are very stable. It does not use any cache (ie: innodb buffer pool) either. The result is also interesting: utilizing multiple threads (up to 16 threads) and reading data from disk (using disk cache, probably) can be ~10x faster compared to reading from memory in a single thread.

Result: ~10x performance gain, no index used

Query 1b: simple, avg

Summary of simple query performance

Here is what we learned comparing Aurora PQ performance to native MySQL query execution:

  1. Select count(*), not using index: 10x performance increase with Aurora PQ.
  2. select avg(…), not using index: 2x performance increase with Aurora PQ.

Query 2: Complex filter, single table

The following query will always be slow in MySQL. This combination of the filters in the WHERE condition makes it extremely hard to prepare a good set of indexes to make this query faster.

Let’s compare the query performance with and without PQ.

PQ disabled:

10 rows in set (3 min 42.47 sec)

/* another run */

10 rows in set (3 min 46.90 sec)

This query is 100% cached. Here is the graph from PMM showing the number of read requests:

  1. Read requests: logical requests from the buffer pool
  2. Disk reads: physical requests from disk

Buffer pool requests:

Buffer pool requests from PMM

Now let’s enable and force PQ:

PQ enabled:

Now let’s compare the requests:

InnoDB Buffer Pool Requests

As we can see, Aurora PQ is almost NOT utilizing the buffer pool (there are a minor number of read requests. Compare the max of 4K requests per second with PQ to the constant 600K requests per second in the previous graph).

Result: ~8x performance gain

Query 3: Complex filter, join “reference” table

In this example I join two tables: the main “ontime” table and a reference table. If we have both tables without indexes it will simply be too slow in MySQL. To make it better, I have created an index for both tables and so it will use indexes for the join:


PQ disabled, explain plan:

As we can see MySQL uses indexes for the join. Response times:

/* run 1 – cold run */

10 rows in set (29 min 17.39 sec)

/* run 2  – warm run */

10 rows in set (2 min 45.16 sec)

PQ enabled, explain plan:

As we can see, Aurora does not use any indexes and uses a parallel scan instead.

Response time:

Result: ~5x performance gain

(this is actually comparing the index cached read to a non-index PQ execution)


Aurora PQ can significantly improve the performance of reporting queries as such queries may be extremely hard to optimize in MySQL, even when using indexes. With indexes, Aurora PQ response time can be 5x-10x better compared to the non-parallel, fully cached operations. Aurora PQ can help improve performance of complex queries by performing parallel reads.

The following table summarizes the query response times:

Query Time, No PQ, index Time, PQ
select count(*) from ontime where flightdate > ‘2017-01-01’ 2 min 48.81 sec 16.53 sec
select avg(DepDelay) from ontime; 2 min 49.95 sec 1 min 48.17 sec

FlightDate, UniqueCarrier as carrier, FlightNum, Origin, Dest

FROM ontime


DestState not in (‘AK’, ‘HI’, ‘PR’, ‘VI’)

and OriginState not in (‘AK’, ‘HI’, ‘PR’, ‘VI’)

and flightdate > ‘2015-01-01’

and ArrDelay < 15

and cancelled = 0

and Diverted = 0

and DivAirportLandings = 0

ORDER by DepDelay DESC


3 min 42.47 sec 28.49 sec

FlightDate, UniqueCarrier, TailNum, FlightNum, Origin, OriginCityName, Dest, DestCityName, DepDelay, ArrDelay

FROM ontime_ind o

JOIN carriers c on o.carrier = c.carrier_code


(carrier_name like ‘United%’ or carrier_name like ‘Delta%’)

and ArrDelay > 30

ORDER by DepDelay DESC


2 min 45.16 sec 28.78 sec

Photo by Thomas Lipke on Unsplash


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Comments (2)

  • james Reply


    January 18, 2019 at 4:05 am
  • Scott Reply

    How does Aurora parallel query compare to MySQL for queries that index well? The above comparison sounds like it is skewed since it is focusing on non indexable queries. Perhaps I am missing something. Perhaps APQ is not appropriate for well indexed queries.

    March 29, 2019 at 4:23 pm

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