making mysql flexible with parelastic database scalability, amrith kumar, founder cto, parelastic

Scalability and database virtualization

How virtualizing your databases improves performance, and lowers costs

New York City MySQL Meetup, October 3, 2013

What’s this presentation about?

• Scalability and the database tier• What’s the problem?

• How did we get here?

• Some proposed solutions

• What are parallel databases?

• What’s ParElastic?

• How do I get ParElastic?

• Q&A

Scalability and the database tier | NYC MySQL Meetup 2October 3, 2013

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What is the scalability problem?

October 3, 2013 Scalability and the database tier | NYC MySQL Meetup 3

What is the scalability problem?

• Has many faces• Connections and Concurrency

• Data Volume and Retention Period

• Databases and Tenants

• Read vs. Write

• Your problem(s)• May be more than one

• May change over time


Connections and Concurrency

• More [Active] Connections• Worse Performance

• Sizing your database


Data Volume and Retention Period

• Longer Retention Period• More Data

• More Data• Worse Performance

• Progressive deterioration• All data in memory

• All indexes in memory

• Not enough memory


Databases and “Tenants”

• Common paradigm in SaaS applications• Each tenant’s application instance has a database

• Several databases on each database instance

• More databases per instance• Worse Performance


In one customer engagement we were informed that no more than 1000 tenants could be located on one database instance before performance became unacceptable

Read vs. Write

• Simple read (SELECT) queries could scale well• Key based lookups

• With favorable indexes

• Things that cause heartburn• Complex joins (with large data sets)

• Sorts

• Aggregation

• Reads are easier to scale than writes


How did we get here?A brief history lesson


How did we get here? [1]

• A combination of factors• Changes in the application user/usage

• Driven by the Internet and mobile computing

• “News Cycles” are getting shorter

• Economics• Commodity computing is cheap and getting cheaper

• Solutions that can “scale-out” win, others lose

• Ability to leverage higher core-densities• Other databases does a better job at this than MySQL

• MySQL would do great if you had a 20GHz processor ;)



• The Evolution of the Database Management System• A battle between “generalized” and “specialized”

• The Relational Database Management System (RDBMS)• Designed for monolithic systems

• SMP

• Scale-Up

• Applications evolve quickly!• Databases respond slowly



• Moore’s Law• Scale-Up seemed like a fine answer

• But there are limits …



• Database architectures traditionally were• Shared CPU/Memory/Disk

• Also known as “Shared-Everything”

• But “Shared-Everything” doesn’t scale • At least not for databases


A server costing twice as much doesn’t always give you twice as much database “power”. You reach a point of diminishing returns.


• You can pay more but you may not get more


Source: Amazon RDS TPC-C Benchmark. Md. Borhan Uddin, Bo He, Radu Sion, Cloud Computing Center, SUNY Stony Brook. Viewed online http://digitalpiglet.org/research/sion2010cloud-rds.pdf

Some proposed solutions


Some proposed solutions

• Several strategies have been advocated• Cache, Cache, Cache,…

• Get a bigger server [a.k.a. Scale-Up]

• Sharding [a form of Scale-Out]

• NoSQL or NewSQL [typically Scale-Out]

• Replication and variants

• We look at each one in more detail


Cache, Cache, Cache!


caching transitive verb to cache

cache noun

Temporary computer storage used for quick retrieval of data in order to increase processing speed.

• Caching only addresses ‘read’; not ‘write’

• Social Media workloads are 'write heavy‘, 'interactive‘ and ‘highly personalized’

That’s easy! Do some caching!

Get a bigger server [Scale-Up]


I will use a bigger database

server

Can I even get a bigger server?

What if m2.4xlarge isn’t

enough?

Maybe I just have too much

data?

Maybe I have too many users?

Sharding [a form of Scale-Out]


shard noun \ˈshärd\

a piece or fragment of a brittle substance <shards of glass>; broadly : a small piece or part

sharding noun \ˈshär-diŋ\

(a) to make ones application brittle or fragmented;

(b) to take one big problem and make many small problems;

(c) to complicate an application while claiming to solve a scalability problem;

(d) to decrease developer productivity;

(e) a bad idea;

(f) sharding library: a mechanism that attempts (unsuccessfully) to hide the bad taste of sharding

Sharding will solve my problem!

NoSQL or NewSQL?


• Yes, I have to rewrite my application

• Yes, not all queries will work

• No, there’s no standard query language

• No, most do not have ACID guarantees; hell some don’t even guarantee Durability

• Yes, most are somewhat untried science-experiments

• More flavors than Ben & Jerry’s Ice Cream [yes, really]

• But, all the cool kids are doing it!

You need NoSQLor NewSQL!

Replication and variants

• Replication based solutions (typically called clustering)• Many copies of the data

• Distribute queries across the copies

• Keep the copies synchronized: like herding cats

• Write bottleneck

• Read/Write splitting• Single Master (gets all the writes)

• Many Slaves (share the reads)

• Unpredictable latency

• Write bottleneck


What about MySQL Cluster?

• MySQL Cluster is a strange beast

• For best results, you must use the NDB interface

• Only supports the NDB storage engine

• Primarily a distributed in-memory Key-Value Store• That is ACID compliant and supports joins and things if you

use the SQL interface

• But no one tells you about the performance of this path!

• Published benchmarks are all “FlexAsync” which talk directly to the NDB interface• And READ-ONLY


For more details visit http://www.parelastic.com/blog/mysql-cluster-and-benchmarksOr stick around after the presentation and we can chat!

http://www.parelastic.com/blog/mysql-cluster-and-benchmarks

What are parallel databases?


What are parallel databases?

• A database architecture proposed in 19921

• Very successfully applied to many database problems• Oracle Exadata, Netezza, Teradata, Greenplum, …

• An example of the “Shared Nothing” database paradigm

2


1Parallel Database Systems: The future of high performance database processing [1992, Dewitt, Gray,

ftp://ftp.cs.wisc.edu/pub/techreports/1992/TR1079.pdf]2

The Case for Shared Nothing [1986, Stonebraker, http://db.cs.berkeley.edu/papers/hpts85-nothing.pdf]

ftp://ftp.cs.wisc.edu/pub/techreports/1992/TR1079.pdf

How parallel databases execute queries


Image from “Parallel Database Systems: The future of high performance database processing” [1992, Dewitt, Gray, ftp://ftp.cs.wisc.edu/pub/techreports/1992/TR1079.pdf]


Benefits of parallel databases

• Linear improvement in “reads”

• Linear improvements in “writes”

• Better than linear improvement in “joins”

• Better than linear improvement in “aggregation”

• Better than linear improvement in “sorts”


For more details, refer “Parallel Database Systems: The future of high performance database processing” [1992, Dewitt, Gray, ftp://ftp.cs.wisc.edu/pub/techreports/1992/TR1079.pdf]


Parallel Databases vs. Sharding


• Parallel Database• Database architecture• Application is data

location agnostic• Application perceives a

single database• Requires no application

rewrites

• Application is notconstrained by parallel database architecture

• A parallel database handles any schema

• Sharding• Application architecture

• Application is data location aware

• Application perceives a collection of databases

• Requires application rewrites

• Application is constrained to the limitations of the sharding architecture

• Not all schemas are shard’able

What is ParElastic?Hypervisor for databases


What is ParElastic?

• An approach to relational database virtualization

• Addresses issues of scalability in relational databases

• A parallel database architecture• Built on standard MySQL or MySQL variant databases

• Horizontal Scalability

• Elastic


ParElastic: System Architecture

10/7/2013Flex Your Database | ParElastic ® Database Virtualization

Engine30

ParElastic Architecture protected by US8214356, “Apparatus for elastic database processing with heterogeneous data”

Data Distribution: How it works

• User data is “distributed” across multiple storage nodes

• Queries are executed in parallel by some [or all] nodes

• Multiple distribution models supported• Range

• Hash

• Broadcast

• Random

• ParElastic guarantees co-location and query execution


Storage Elasticity: How it works

• A “generational scheme”

• Storage Nodes added over time• Each creates a new “generation”

• Unnecessary to migrate large amounts of data • A key drawback with “sharding” that requires “resharding”


Storage Elasticity protected by US8478790, US8386532 and other patents.

ParElastic: How It Works


Engine33

ParElastic: Simple query processing example

SELECT COUNT(*)

FROM CUSTOMER;

count(*)

--------

2771

(1 row affected)

PROVISION 1 DYNAMIC NODE

ON DYNAMIC NODE

CREATE TEMP TABLE

T1

( C INT );

ON ALL STORAGE NODES

SELECT COUNT(*)

FROM CUSTOMER

AND REDISTRIBUTE

TO T1

ON DYNAMIC NODE

SELECT SUM(C)

FROM T1;


Engine34

ParElastic Performance Benefits

• Connection Scalability• ParElastic Tier Elasticity; have more or less ParElastic servers

• Storage / Data Volume Scalability• Add ParElastic Persistent Nodes as data volumes increase

• Multiple machines working together

• Workloads are variable• Compute Node Elasticity; have more or less as required

• Databases and Tenants [SaaS applications]• ParElastic Adaptive Multi-tenancy ™

• No application change

• Queries processed by, data stored on standard MySQL!


Engine35

ParElastic Multi-Tenancy


ParElastic Concurrency [1]


ParElastic Concurrency [2]


ParElastic data “ingest”


Tests conducted in Amazon Cloud. Native MySQL testing on m1.xlarge server, standard MySQL, standard EBS volumes. Test driver was a c1.xlarge server to provide sufficient CPU head-room to generate load. ParElastic run with 5 and 15 persistent storage nodes identically configured, m1.xlarge, standard MySQL, standard EBS Volumes. 15 node test employed two c1.xlarge test drivers. Best ParElastic performance was with 10 threads, 10 persistent storage nodes and an insert batch size of 5,000 tuples per insert batch. Best native MySQL performance was with 2 threads and a batch size of 10,000 tuples per insert batch.

One Million rows/s!15 Storage Nodes, 2 ParElastic Servers

What’s the ParElastic Overhead?


Machine 1

Test Client

Machine 2

mysqld

Machine 1

Test Client

Machine 3

mysqld

Machine 4

mysqld…

Machine 2

ParElastic

Network RTT 0.35ms

Query Time 15.72ms

Query Time 17.03ms

ParElastic overhead ~ 1.31ms

Characterizing ParElastic Performance

• A “fixed cost”, the overhead per query

• A “variable cost” for query processing

• Consider this example, a simple “COUNT” query.


Some things to keep in mind

• Horizontal Scale-Out benefits from• Being “stateless”, or at least having less state

• Adhering to a truly “shared nothing” approach

• Horizontal Scale-Out is impeded by• Complex or Shared “State”

• Things that violate the “shared nothing” paradigm


What is ParElastic?

• An approach to relational database virtualization• "A Hypervisor for the Database Tier"

• Scale out database capacity across many servers• Effectively handle workloads too big for one server

• Share this pool of database among many applications• Efficiently allocate database capacity to workload

• An elastic, multi-tenant, parallel database architecture• Built on standard MySQL or MySQL variant databases

• Horizontal Scalability

• Elastic


Some target markets

• Database Virtualization – “Hypervisor for the Database”• Reduce capex and simplify administration for development

and test

• SaaS Enablement• Simplified deployment of SaaS applications using multi-

tenancy

• High Volume Database Applications• High traffic websites, (e.g. social, ecommerce, on-line games)

• High speed data ingest (e.g. click tracking, sensor arrays, mobile)


Where do I get ParElastic?


Getting ParElastic

• For Evaluations• Available at no charge on Amazon Marketplace

• Preconfigured for evaluation purposes; not performance testing

• Runs completely on a single EC2 instance

• For Larger Configurations• Contact ParElastic

• Email: [email protected]

• Twitter: @parelastic

• Web: http://www.parelastic.com


mailto:[email protected]

Getting ParElastic

• On the Amazon AWS Marketplace (aws.amazon.com/marketplace)

• Quick start guide and simple (two-step) setup wizard provided.


Conclusion


Conclusion

• Database Scalability is a very real problem• The Cloud has put a very complicated wrinkle in it

• The problem was seen before with commodity servers• Virtualization was able to address this problem

• Several “hacks” have been proposed• Not really solutions, just hacks

• ParElastic is a database virtualization solution• Based on standard relational databases

• Provides benefits of horizontal scalability and multi-tenancy

• ParElastic is available for evaluation on many platforms• Free evaluation also available on Amazon Marketplace


Contacting ParElastic


• Look us up online– http://www.parelastic.com

• Watch an explainer video– http://www.parelastic.com/video

• Contact us– Email: [email protected]

http://www.parelastic.com/

http://www.parelastic.com/video

Q&A


Image Credits• Moore’s Law

• Wikipedia [http://commons.wikimedia.org/wiki/File%3ATransistor_Count_and_Moore's_Law_-_2011.svg]

• Hercules slays the Hydra

• Wikipedia [http://commons.wikimedia.org/wiki/File%3AHercules_slaying_the_Hydra.jpg]

• CPU History

• Phillip E. Ross, “Why CPU Frequency Stalled” [http://spectrum.ieee.org/computing/hardware/why-cpu-frequency-stalled]

• Herding Cats

• Image from [http://wodongatafe.wordpress.com/2011/05/27/herding-cats-or-facilitating-a-webinar-whats-the-difference/]


making mysql flexible with parelastic database scalability, amrith kumar, founder cto, parelastic

Technology