2. Topics
● What is Redis?
● The Redis Foreign Data Wrapper
● The Redis Command wrapper for Postgres
● Case study – a high performance Ad server
using Postgres and Redis
4. Redis is easy to use
● Almost no configuration
● On Fedora
sudo yum install redis
sudo systemctl enable redis.service
sudo systemctl start redis.service
redis-cli
6. Redis data values
● Values can be scalars
● Strings
● Integers
● Values can be structured
● Lists
● Sets
● Ordered sets
● Hashes – name value pairs
– c.f. Hstore
7. Simple command set
● Nothing like SQL, table joins
● Command set is large but most commands only
take 2 or 3 parameters
● http://redis.io/commands
9. No creation command
● You create an object by setting or adding to it
● Almost schema-less
● Can't use a command for one object to another
10. Redis keys all live in a single global
namespace
● No schemas
● No separation by object type
● Very common pattern is to use fine grained
keys, like (for a web session)
web:111a7c9ff5afa0a7eb598b2c719c7975
● KEYS command can find keys by pattern:
● KEYS web:*
– Dangerous
11. How Redis users do “tables”
● They use a prefix:
● INCR hits:2013.05.25
● They can find all these by doing
● KEYS hits:*
● Or they keep a set with all the keys for a given
type of data
● SADD hitkeyset hits:2013.05.25
● The application has to make use of these keys –
Redis itself won't
15. Updates by me
● All data types supported
● Table key prefixes supported
● Table key sets supported
● Array data returned as a PostgreSQL array
literal
16. Hash tables
● Most important type
● Most like PostgreSQL tables
● Best to define the table as having array of text
for second column
● Turn that into json, hstore or a record.
17. Example
● CREATE FOREIGN TABLE web_sessions(
key text,
values text[])
SERVER localredis
OPTIONS (tabletype hash,
tablekeyprefix 'web:');
SELECT * from web_sessions;
18. Use with hstore
● CREATE TYPE websession AS (
id text,
browser text,
username text);
SELECT populate_record(null::websession,
hstore(values))
FROM websessions;
19. Use with json_object
● https://bitbucket.org/qooleot/json_object
● CREATE EXTENSION json_object;
SELECT json_object(values)
FROM websessions;
20. Key prefix vs Key Set
● Key sets are much faster
● Ad server could not meet performance goals
until it switched to using key sets
● Recommended by Redis docs
21. Using a key set to filter rows
● Sort of “where” clause
● Put the keys of the entries you want in a set
somehow
● Can use command wrapper
● Define a new foreign table that uses that set
as the keyset
22. 9.3 notes
● In 9.3 there is json_populate_record()
● Could avoid use of hstore
● For post 9.3, would be a good idea to have a
function converting an array of key value pairs
to a record directly
23. Brand new – Singleton Key tables
● Each object is a table, not a row
● Sets and lists come back as single field rows
● Ordered sets come back as one or two field
rows
– second field can be score
● Hashes come back as rows of key/value
25. Redis Command Wrapper
● Fills in the missing gaps in functionality
● Sponsored by IVC: http://www.ivc.com
● https://bitbucket.org/qooleot/redis_wrapper
27. redis_connect()
● First argument is “handle”
● Remaining arguments are all optional
● con_host text DEFAULT '127.0.0.1'::text
● con_port integer DEFAULT 6379
● con_pass text DEFAULT ''::text
● con_db integer DEFAULT 0
● ignore_duplicate boolean DEFAULT false
28. Redis wrapper connections are
persistent
● Unlike FDW package, where they are made at
the beginning of each table fetch
● Makes micro operations faster
29. redis_command and
redis_command_argv
● Thin layers over similarly named functions in
client library
● redis_command has max 4 arguments after
command string – for more use
redis_command_argv
● Might switch from VARIADIC text[] to
VARIADIC “any”
30. Uses
● Push data into redis
● Redis utility statements from within Postgres
31. Higher level functions
● redis_push_record
● con_num integer
● data record
● push_keys boolean
● key_set text
● key_prefix text
● key_fields text[]
33. Our use case
● An ad server for the web
● If Redis crashes, not a tragedy
● If it's slow, it's a tragedy
34. Ad Server Project by IVC
http://www.ivc.com
Remaining slides are mostly info from IVC
35. System Goals
● Serve 10,000 ads per second per application server
cpu
● Use older existing hardware
● 5 ms for Postgres database to filter from 100k+ total
ads to ~ 30 that can fit a page and meet business
criteria
● 5 ms to filter to 1-5 best ads per page using statistics
from Redis for freshness, revenue maximization etc.
● Record ad requests, confirmations and clicks.
● 24x7 operation with automatic fail over
38. Postgres databases
● 6 Postgres databases
● Two for business model – master and streaming hot
standby (small VM)
● Two for serving ads – master and streaming hot
standby (physical Dell 2950)
● Two for for storing clicks and impressions – master
and hot standby (physical Del 2950)
● Fronted by redundant pg pool load balancers with fail
over and automated db fail over.
39. Business DB
● 30+ tables
● Example tables: ads, advertisers, publishers, ip
locations
● Small number of users that manipulate the data (<
100)
● Typical application and screens
● Joining too slow to serve ads
● Tables get materialized into 2 tables in the ad serving
database
40. ● Two tables
● First has ip ranges so we know where the user is
coming from. Ad serving is often by country, region
etc.
● Second has ad sizes, ad types, campaigns,
keywords, channels, advertisers etc.
● Postgres inet type and index was a must have to be
successful for table one
● Tsquery/tsvector, boxes, arrays were all a must have
for table two (with associated index types)
Ad Serving Database
41. Ad serving Database
● Materialized and copied from Business
database every 3 minutes
● Indexes are created and new tables are
vacuum analyzed then renamed.
● Performance goals were met.
● We doubt this could be done without Postgres
data types and associated indexes
● Thanks
42. Recording Ad requests/confirmations
and clicks
● At 10k/sec/cpu recording ads one row at a time +
updates on confirmation is too slow
● Approach: record in Redis, update in Redis and once
every six minutes we batch load from Redis to
Postgres. - FDW was critical.
● Partitioning (inheritance) with constraint exclusion to
segregate data by day using nightly batch job. One
big table with a month's worth of data would not
work.
● Table partitioning is not cheap in the leading
commercial product.
● Thanks
43. Recording DB continued.
● Used heavily for reporting.
● Statistics tables (number of clicks, impressions
etc.) are calculated every few minutes on
today's data
● Calculated nightly for the whole day tables
● For reporting we needed some business data
so we selectively replicate business tables in
the ad recording database using Skytools. DB
linking tables is too slow when joining.
44. Recording DB cont'd
● Another usage is fraud detection.
● Medium and long term frequency fraud
detection is one type of fraud that this
database is used for.
45. Redis
● In memory Database.
● Rich type support.
● Multiple copies and replication.
● Real time and short term fraud detection
● Dynamic pricing
● Statistical best Ad decision making
● Initial place to record and batch to Postgres
●
Runs on VM with 94Gb of dedicated RAM.
46. Redis cont'd
● FDW and commands reduce the amount of
code we had to write dramatically
● FDW good performance characteristics.
● Key success factor: In memory redis DB +
postgres relational DB.
47. Postgres – Redis interaction
● Pricing data is pushed to Redis from Business
DB via command wrapper
● Impression and Click data is pulled from Redis
into Recording DB via Redis FDW
48. Current Status
● In production with 4 significant customers
since March 1
● Scaling well
49. Conclusions
● Postgres' rich data types and associated
indexes were absolutely essential
● Redis + Postgres with good FDW integration
was the second key success factor
● Node.js concurrency was essential in getting
good application throughput
● Open source allowed the system to be built for
less than 2% of the cost of a competing
commercial system
