Redis memcached pdf

CachingMemcached vs. Redis

San Francisco MySQL Meetup

Ryan LoweErin O’Neill

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Databases

WE LOVE THEM...

Except when we don’t

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When Databases Rule

• Many access patterns on the same set of data

• Transactions (both monetary and isolated units of work)

• Don’t know what the end state access patterns will be

• Always :)

3

When Databases Suck

• Lots of concurrent users

• ORMs

• Big Data Sets

• Small Pockets of VERY hot data

4

How Caching Works

• External vs. built-in caching

• MySQL Query Cache

• InnoDB Buffer Pool

• Rails SOMETHING

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Caching Architecture

Becomes

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Why Caching Works

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There are only two hard problems in Computer Science: cache invalidation, naming things, and off-by-one errors.

-- Martin Fowler

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Problems with Caching

• Cache Misses

• Thundering Herd

• Stale Data

• Warm-Up Times

• Overly-Aggressive Caching

• Poor Cache Design

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Cache Misses

Cache Hit: 1 OperationCache Miss: 3 Operations

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Thundering Herd

• Key TOP_10_VIDEOS expires @ 9:00

• Generating the K/V takes three seconds

• Page gets 100 req/s = 100*3 = 300 threads!

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Stale Data

• Must maintain consistency between the database and the cache from within the application

• Extremely difficult to validate correctness

14

Cache Warm-Up

• All attempts to read from the cache are CACHE MISSES, which require three operations.

• This can result in a significant degradation of response time.

• Usually accompanied by a Thundering Herd

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Use Cases

• Sessions

• Popular Items

• Full Page Cache

• Profile Information

• User Preferences

• Tag Clouds

• Auto-suggest lists

• Relationships

• User Information

• Online Users

• Statistics

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Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

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Redis

Redis is an open source, advanced key-value store. It is often referred to as a “data structure server” since keys can contain strings, hashes, lists, sets and sorted sets.

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In-Memory Means We’re Bound By RAM

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Consistent Hashing

• Each Key deterministically goes to a particular server. Think (KEY % SERVERS)

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Memcached

• Dead Simple & Battle Tested

• Fast

• Non-Blocking get()/set()

• Multi-Threaded

• Consistent Hashing

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Memcached Example

employee_id = 1234

employee_json = {

name => ‘Ryan Lowe’,

title => ‘Production Engineer’ }

set(employee_id, employee_json)

get(employee_id) [Returns employee_json]

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But I don’t want all the data

• What if I just want the name?

• 64 Bytes for the object vs. 10 for just the name :-(

• 6x network traffic

• More work for the application

• Fatter applications

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Redis

• Advanced Data Types

• Replication

• Persistence

• Usually Fast

• Very Cool Atomic Operations

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Redis: The Bad

• Single-Threaded

• Limited client support for consistent hashing

• Significant overhead for persistence (do be discussed later)

• Not widely deployed (compared to Memcached)

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Redis: Datatypes

• Strings (just like Memcached)

• Lists

• Sets

• Sorted Sets

• Hashes

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Redis: Lists

• Stored in sorted order

• Can push/pop

• Fast head/tail access

• Index access (yay)

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Redis: Lists

r.lpush(‘employees’, ‘Ryan Lowe’)

r.lpush(‘employees’, ‘Dave Apgar’)

r.lrange(‘employees’, 0, -1)

(‘Dave Apgar’, ‘Ryan Lowe’)

r.rpush(‘employees’, ‘Evan Miller’)

r.lrange(‘employees’, 0, -1)

(‘Dave Apgar’, ‘Ryan Lowe’, ‘Evan Miller’)

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Redis: Sets

• Un-ordered collections of strings

• Unique (no repeated members)

• diff, intersect, merge

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Redis: Sets

sadd(‘employees’, ‘Ryan Lowe’)

sadd(‘former_employees’, ‘Bryan Lowe’)

sdiff(‘former_employees’, ‘employees’)

(‘Ryan Lowe’,‘Bryan Lowe’)

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Redis: Sorted Sets

• Same as Sets but ordered by a score

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Redis: Hashesr.hset(‘employees’, ‘count’, 1234)

r.hset(‘employees’,‘females’, 1000)

r.hset(‘employees’,‘males’, 234)

hget(‘employees’,‘count’)

“1234”

hgetall(‘employees’)

{ ‘count’ => 1234,

‘females’ => 1000,

‘males’ => 234 }

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Memcached vs. RedisMemcached Redis

(multi)get ✓ ✓(multi)set ✓ ✓incr/decr ✓ ✓

delete ✓ ✓Expiration ✓ ✓

prepend/append ✓Range Queries ✓

Data Types! ✓Persistence (sorta) ✓

Multi-Threaded ✓Replication (sorta) ✓

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Instrumentation

• Redis: info

• Memcached: stats

• Both give system information, connections, hits, misses, etc.

• Graphite most of the metrics!!!

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Benchmarks

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About the Benchmarks

• 1 Hour

• Redis 2.6 & Memcached 1.4.5

• 64,000,000 Keys"KEY_#{i.to_s}"

• 51-Character Values(0...50).map{ ('a'..'z').to_a[rand(26)] }.join

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Redis Benchmarks

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Redis Set (1 Server)

0

1500

3000

4500

6000

7500

9000

10500

12000

1 Client 2 Clients 4 Clients 8 Clients12 Clients 16 Clients 24 Clients 32 Clients

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Redis Set (1 Server)

0

1500

3000

4500

6000

7500

9000

10500

12000

1 Client 2 Clients 4 Clients 8 Clients12 Clients 16 Clients 24 Clients 32 Clients

WTF?!

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Redis Set (1 Server)

0

2000

4000

6000

8000

10000

1 Client 2 Clients 4 Clients 8 Clients12 Clients 16 Clients 24 Clients

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Redis Set (2 Servers)

0

4000

8000

12000

16000

1 Client 2 Clients 4 Clients 8 Clients12 Clients 16 Clients 24 Clients 32 Clients

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Redis Set (2 Servers)

0

4000

8000

12000

16000

1 Client 2 Clients 4 Clients 8 Clients12 Clients 16 Clients 24 Clients 32 Clients

WTF?!

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Redis Set (4 Servers)

0

4000

8000

12000

16000

1 Client 2 Clients 4 Clients 8 Clients12 Clients 16 Clients 24 Clients 32 Clients

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Redis Set (8 Servers)

0

4000

8000

12000

16000

1 Client 2 Clients 4 Clients 8 Clients12 Clients 16 Clients 24 Clients 32 Clients

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Hash Ring Balance (%)

0

50

100

Redis Memcached

Server 1 Server 2

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Hash Ring Balance (%)

0

25

50

Redis Memcached

Server 1 Server 2 Server 3 Server 4

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Hash Ring Balance (%)

0

12.5

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Redis Memcached

Server 1 Server 2 Server 3 Server 4 Server 5Server 6 Server 7 Server 8

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Redis Get (1 Server)

0

25000

50000

75000

100000

125000

1 Client 2 Clients 4 Clients 8 Clients12 Clients 16 Clients 24 Clients 32 Clients

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Redis Get (2 Servers)

0

25000

50000

75000

100000

125000

1 Client 2 Clients 4 Clients 8 Clients12 Clients 16 Clients 24 Clients 32 Clients

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Redis Get (4 Servers)

0

25000

50000

75000

100000

125000

1 Client 2 Clients 4 Clients 8 Clients12 Clients 16 Clients 24 Clients 32 Clients

50

Redis Get (8 Servers)

0

25000

50000

75000

100000

125000

1 Client 2 Clients 4 Clients 8 Clients12 Clients 16 Clients 24 Clients 32 Clients

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The Cost of Persistence

4000

6000

8000

10000

12000

Redis No Persistence Redis BGSAVE (FIO) Redis AOF

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Redis & MemcachedBenchmarks

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Set Operations (1 Server, 24 Clients)

9300

9475

9650

9825

10000

Memcached Redis

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Get Operations(1 Server, 24 Clients)

80000

92500

105000

117500

130000

Memcached Redis

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Set Operations (2 Servers, 24 Clients)

8000

9250

10500

11750

13000

Memcached Redis

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Get Operations (2 Servers, 24 Clients)

8000

41000

74000

107000

140000

Memcached Redis

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Set Operations (4 Servers, 24 Clients)

8000

9250

10500

11750

13000

Memcached Redis

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Get Operations (4 Servers, 24 Clients)

8000

41000

74000

107000

140000

Memcached Redis

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Set Operations (8 Servers, 24 Clients)

8000

9000

10000

11000

12000

Memcached Redis

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Get Operations (8 Servers, 24 Clients)

8000

43500

79000

114500

150000

Memcached Redis

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Conclusions

• Redis inconsistent under heavy load

• We need more benchmarks!

• (Redis) Datatype-specific

• Big Memory

• (Redis) Big Keys

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Questions?

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