Amazon Aurora with MySQL compatibility includes several features to improve query performance, while still maintaining full MySQL compatibility. One such feature is Parallel Query, which provides faster analytic queries over current data by pushing query processing down to thousands of CPUs in the storage layer, achieving performance gains of up to two orders of magnitude. Learn how to take advantage of this and other recent Aurora features to implement high performance distributed queries for your MySQL-based applications.

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Accelerate Your Analytic Queries
with Amazon Aurora Parallel Query
Aakash Shah
Sr. Software Engineer
Amazon Aurora, AWS
D A T 3 6 2
Kamal Gupta
Sr. Software Manager
Amazon Aurora, AWS

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Agenda
1. Amazon Aurora overview
2. Deep dive
3. Performance
4. Customer experience
5. Global databases

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Amazon Aurora
A relational database reimagined for the cloud
 Speed and availability of high-end commercial databases
 Simplicity and cost-effectiveness of open source databases
 Drop-in compatibility with MySQL and PostgreSQL
 Simple pay as you go pricing
Delivered as a managed service
Amazon Aurora

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Scale-out, distributed architecture
Master Replica Replica Replica
AVAILABILITY
ZONE 1
SHARED STORAGE VOLUME
AVAILABILITY
ZONE 2
AVAILABILITY
ZONE 3
STORAGE NODES WITH SSDS
• Logging pushed down to a purpose-
built log-structured distributed
storage system
• Storage volume is striped across
hundreds of storage nodes
distributed across 3 availability
zones (AZ)
• Six copies of data, two copies in
each AZ
SQL
TRANSACTIONS
CACHING
SQL
TRANSACTIONS
CACHING
SQL
TRANSACTIONS
CACHING

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The journey so far
• AZ+1 tolerance
• Continuous data backup
• Backtrack
• Instant redo recovery
• Read-replicas with
failover order
• Continuous availability
with Multi-Master
• Global databases
• ZDP
• Serverless
• Auto volume growth
• Performance insights
• Read replica auto scaling

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QP improvements
Feature Workload type Operation type
LRA Out of memory Scan
Batch scan In memory Scan
AKP Out of memory Non equi-joins
Hash joins In memory & out of
memory
Equi-joins

9. But what about read latencies of long running
queries? Can we do better?

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Netflix
Netflix is the world's leading internet entertainment service with over 130
million memberships in over 190 countries enjoying TV series,
documentaries and feature films across a wide variety of genres and
languages.
“We were able to test Aurora’s Parallel Query feature and the
performance gains were very good. To be specific, for queries doing full
table scan or fetching fat indexes with billions of rows, we noticed the
query time reduced from 32 minutes to 3 minutes. We were able to
reduce the instance type from r3.8xlarge to r3.2xlarge. For this use-case,
Parallel Query was a great win for us.” —Jyoti Shandil, Cloud Data
Architect.

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TransNexus
TransNexus is a VOIP software development firm providing fraud
detection, intelligent routing, and analytics solutions for major carriers
worldwide.
“We tested Aurora’s Parallel Query feature with analytics applications
within our ClearIP software product hosted in AWS. We’ve been excited to
find that larger, more intensive queries perform up to 20x faster with
Parallel Query turned on.” —Alec Fenichel, Software Developer.

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Amazon Aurora PQ benefits
• Fully managed
• Scales with storage
• No special hardware required
• No pre-provisioning required
• No setup and tuning required

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Amazon Aurora PQ
Amazon Aurora Storage has thousands of
CPUs
• Presents opportunity to push down and parallelize
query processing using the storage fleet.
• Moving processing close to data reduces network
traffic and latency.
However there are significant challenges
• Data stored in storage node is not range partitioned
– require full scans.
• Data may be in-flight.
• Read views may not allow viewing most recent data.
• Not all functions can be pushed down to storage
nodes.
DATABASE NODE
STORAGE NODES
PUSH DOWN
PREDICATES
AGGREGATE
RESULTS

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Database node processing
Query Optimizer produces PQ Plan and
creates PQ context based on leaf page
discovery.
PQ request is sent to storage node along
with PQ context.
Storage node produces:
• Partial results streams with processed stable
rows.
• Raw stream of unprocessed rows with pending
undos.
Head node aggregates these data streams
to produce final results.
STORAGE NODES
OPTIMIZER
EXECUTOR
INNODB
NETWORK STORAGE DRIVER
AGGREGATOR
APPLICATION
PARTIAL
RESULTS
STREAM
RESULTS
IN-FLIGHT
DATA
PQ CONTEXT
PQ PLAN

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Storage node processing
Each storage node runs up to 16 PQ
processes, each associated with a parallel
query.
PQ process receives PQ context
• List of pages to scan.
• Read view and projections.
• Expression evaluation byte code.
PQ process makes two passes through the page list
• Pass 1: Filter evaluation on InnoDB formatted raw
data.
• Pass 2: Expression evaluation on MySQL
formatted data.
PQ PROCESS
PQ PROCESS
Up to 16
STORAGE
NODE PROCESS
PAGE LISTS
TO/FROM HEAD NODE

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Amazon Aurora PQ summary
Performance
120x lower latencies on TPCH-like benchmarks with Improved I/O performance and reduced CPU
usage on the head node.
High Concurrency
Run both OLTP and light OLAP workloads simultaneously and efficiently.
Cost Effective
PQ comes at no extra cost. Can run on your live data. Potentially reduced effort and data
duplication in your ETL pipeline.
Quiet Tenant
Reduced chance of evicting frequently used pages from the buffer pool that are used by OLTP
workload.
Ecosystem
Get Aurora goodies such as PiTR, Continuous backup, Fast Cloning with PQ.

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Well-known decision support benchmark
0x
2x
4x
6x
8x
10x
12x
14x
16x
18x
20x
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Query response time reduction
 Peak speed up ~18x
 >2x speedup: 10 of 22 queries
Performance: QP latency gains

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Performance: PQ latency gains
Well-known decision support benchmark
0x
20x
40x
60x
80x
100x
120x
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Query response time reduction
 Peak speed up ~120x
 >10x speedup: 8 of 22 queries

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0x
40x
80x
120x
160x
200x
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Query response time reduction
 Peak speed up ~187x
 >10x speedup: 10 of 22 queries
Performance: Combined latency gains
Well-known decision support benchmark

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Parallel Query: Performance results

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How to get started with PQ?
• Create new clusters or restore existing 5.6 clusters.
• Customers can verify that PQ feature is available using select
@@aurora_pq_supported;
• PQ can be statically enabled or disabled for the cluster by using aurora_pq in
the cluster parameter group.
• PQ can dynamically be enabled or disabled per session using set session
aurora_pq = {'ON'/'OFF’}.
• Smart Optimizer automatically selects PQ.

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Verifying PQ
mysql> explain select p_name, p_mfgr from part
-> where p_brand is not null
-> and upper(p_type) is not null
-> and round(p_retailprice) is not null;
+----+-------------+-------+...+----------+------------------------------
----------------------------------------------+
| id | select_type | table |...| rows | Extra
|
+----+-------------+-------+...+----------+------------------------------
----------------------------------------------+
| 1 | SIMPLE | part |...| 20427936 | Using where; Using parallel
query (5 columns, 1 filters, 2 exprs; 0 extra) |
+----+-------------+-------+...+----------+------------------------------
----------------------------------------------+

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PQ status variables
• Aurora_pq_request_attempted
• Aurora_pq_request_executed
• Aurora_pq_request_failed
• Aurora_pq_pages_pushed_down
• Aurora_pq_bytes_returned
• Aurora_pq_request_not_chosen
• Aurora_pq_request_not_chosen_below_min_rows

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Current limitations
• Amazon Aurora PQ currently only available with Aurora MySQL 5.6.
Integration with 5.7 and Postgress will follow.
• Incompatible with db.t2 instance types.
• Available in 5 regions: US East (Virginia, Ohio), US West (Oregon), EU
(Ireland) and Asia Pacific (Tokyo). More regions to follow.
• Integration with Performance Insights and Backtrack will follow.

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Global replication
Faster disaster recovery and enhanced data locality

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High throughput: Up to 200K writes/sec – negligible performance impact
Low replica lag: < 1 sec cross-country replica lag under heavy load
Fast recovery: < 1 min to accept full read-write workloads after region failure
Global replication–Aurora physical
MR R
REGION 1
AZ 1 AZ 2 AZ 3
SHARED STORAGE
R
REGION 2
AZ 1 AZ 2 AZ 3
SHARED STORAGE
REPLICATION
FLEET
REPLICATION
FLEET

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Global replication performance
Logical vs. physical replication
Logical replication with MTS Physical replication
0
100
200
300
400
500
600
0
50,000
100,000
150,000
200,000
250,000
seconds
QPS
Series1
Series2
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
0
50,000
100,000
150,000
200,000
250,000
seconds
QPS
Series1
Series2
SysBench OLTP (write-only) stepped every 600 seconds on R4.16xlarge

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33. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Aakash Shah
aakashah@amazon.com
Kamal Gupta
kamalg@amazon.com

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