scaling crashlytics: building analytics on redis 2.6

© 2012. All rights reservedCRASHLYTICS CONFIDENTIAL

Redis Analytics

@JeffSeibertCEO, Crashlytics

2

Crashlytics for Mac


Strings

Lists

Hashes

Sets

Sorted Sets

8


Strings

Lists

Hashes

Sets

Sorted Sets

9

Activity Tracking

Event Tracking

Leader boards


Active User Tracking


CREATE TABLE accounts ( id int(11) NOT NULL AUTO_INCREMENT PRIMARY KEY, name varchar(255), email varchar(255), ...

last_active_at datetime);

11



CREATE TABLE events ( id int(11) NOT NULL AUTO_INCREMENT PRIMARY KEY, type varchar(32), account_id int(11), happened_at datetime);

12




14

0 0 0 0 1 0 0 1

accounts::active

SETBIT key offset value (>= 2.2) O(1) > SETBIT “accounts::active” 4 1> SETBIT “accounts::active” 7 1



15

1 1 1 0 1 0 1 1

accounts::active::2012-10

0 0 1 0 1 0 0 1

accounts::active::2012-10-22

0 0 0 0 1 0 0 1

accounts::active::2012-10-22-00



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def record_active(obj, t=Time.now.utc) key = "#{obj.class.name.downcase.pluralize}::active::" key << t.year.to_s key << "-" << '%02d' % t.month REDIS.setbit key, obj.id, 1 # accounts::active::2012-10 key << "-" << '%02d' % t.day REDIS.setbit key, obj.id, 1 # accounts::active::2012-10-22 key << "-" << '%02d' % t.hour REDIS.setbit key, obj.id, 1 # accounts::active::2012-10-22-00end


‣ We want to know…• How many users were active today? This month?

• Was user X active today? This month?


BITCOUNT key (>= 2.6) O(N) > BITCOUNT “accounts::active::2012-10-22”(integer) 3> BITCOUNT “accounts::active::2012-10”(integer) 5

GETBIT key index (>= 2.2) O(1) > GETBIT “accounts::active::2012-10-22” 6(integer) 0> GETBIT “accounts::active::2012-10” 6(integer) 1


‣ Graphs and Heatmaps• Monthly actives over time?

• Over time, when was user X active?


> GETBIT “accounts::active::2012-10-22” 6> GETBIT “accounts::active::2012-10-21” 6> GETBIT “accounts::active::2012-10-20” 6> GETBIT “accounts::active::2012-10-19” 6...

> BITCOUNT “accounts::active::2012-07”> BITCOUNT “accounts::active::2012-08”> BITCOUNT “accounts::active::2012-09”> BITCOUNT “accounts::active::2012-10”...


‣ Advanced Data-Mining: WAU• Computing weekly active users:

•


BITOP op destkey srckey [srckeys...] (>= 2.6) O(N) > BITOP OR “accounts::active::2012-W42” \ “accounts::active::2012-10-21” \ “accounts::active::2012-10-20” \ “accounts::active::2012-10-19” \ “accounts::active::2012-10-18” \ “accounts::active::2012-10-17” \ “accounts::active::2012-10-16” \ “accounts::active::2012-10-15”> BITCOUNT “accounts::active::2012-W42”


‣ Advanced Data-Mining: Retention• What % of users active last week are active this week?

•


BITOP op destkey srckey [srckeys...] (>= 2.6) O(N) > BITOP AND “accounts::active::2012-W41+W42” \ “accounts::active::2012-W41” \ “accounts::active::2012-W42”> BITCOUNT “accounts::active::2012-W41”> BITCOUNT “accounts::active::2012-W41+W42”


‣ Advanced Data-Mining: Churn• Locate accounts that have been inactive for 3 months

•


BITOP op destkey srckey [srckeys...] (>= 2.6) O(N) > BITOP OR “accounts::active::2012-Q3” \ “accounts::active::2012-09” \ “accounts::active::2012-08” \ “accounts::active::2012-07”> BITOP NOT “accounts::churned::2012-Q3” \ “accounts::active::2012-Q3”> BITCOUNT “accounts::churned::2012-Q3”


def record_boolean(obj, topic=:active, t=Time.now.utc) key = "#{obj.class.name.downcase.pluralize}::#{topic}::" key << t.year.to_s key << "-" << '%02d' % t.month REDIS.setbit key, obj.id, 1 # accounts::active::2012-10 key << "-" << '%02d' % t.day REDIS.setbit key, obj.id, 1 # accounts::active::2012-10-22 key << "-" << '%02d' % t.hour REDIS.setbit key, obj.id, 1 # accounts::active::2012-10-22-00end



Event Tracking


0 0 0 0 ? 0 0 0

apps::crashes

Event Tracking


apps::crashes { 0 => 34, 1 => 546457, 2 => 1}

HINCRBY key field increment (>= 2.0) O(1) > HINCRBY “apps::crashes” “0” 1> HINCRBY “apps::crashes” “2” 1

Event Tracking


app::0::crash::by_day { 2012-10-22 => 34, 2012-10-21 => 46, 2012-10-20 => 29, ...}

> HINCRBY “app::0::crash::by_day” “2012-10-22” 1

Event Tracking


def record_event(obj, topic=:crash, specificity=:day, t=Time.now.utc) key = "#{obj.class.name.downcase}::#{obj.id}::#{topic}::by_#{specificity}" # e.g. app::0::crash::by_day field = t.year.to_s field << "-" << '%02d' % t.month # 2012-10 REDIS.hincrby key, field, 1 if specificity == :month field << "-" << '%02d' % t.day # 2012-10-22 REDIS.hincrby key, field, 1 if specificity == :day field << "-" << '%02d' % t.hour # 2012-10-22-00 REDIS.hincrby key, field, 1 if specificity == :hour end

Event Tracking


‣ We want to…• Power a graph of crashes over the last week

• “Zoom” the graph to see more detail

HMGET key field1 [...] (>= 2.0) O(N) > HMGET “app::0::crash::by_day” “2012-10-22” \ “2012-10-21” “2012-10-20” “2012-10-19” \

“2012-10-18” “2012-10-17” “2012-10-16”1) ...

> HMGET “app::0::crash::by_hour” “2012-10-22-00” \ “2012-10-22-01” “2012-10-22-02” “2012-10-22-03” \

“2012-10-22-04” “2012-10-22-05” “2012-10-22-06” ...1) ...

Event Tracking


Grouped Event Tracking

“How often has app X crashed on each type of iPad?”


app::0::crash::iPad1,1 { 2012-10-22 => 34, 2012-10-21 => 46, 2012-10-20 => 29, ...}

app::0::crash::iPad2,1 { 2012-10-22 => 12, 2012-10-21 => 17, 2012-10-20 => 11, ...}

device_models [ “iPad1,1”, “iPad2,1”, ...]



app::0::crash::2012-10-22 { ALL => 46, iPad1,1 => 34, iPad2,1 => 12, ...}

HGETALL key (>= 2.0) O(N) > HGETALL “app::0::crash::2012-10-22”(multi-bulk)



def record_grouped_event(obj, group, topic=:crash, t=Time.now.utc) key = "#{obj.class.name.downcase}::#{obj.id}::#{topic}::" key = t.year.to_s key << "-" << '%02d' % t.month # app::0::crash::2012-10 REDIS.hincrby key, group, 1 REDIS.hincrby key, 'ALL', 1 field << "-" << '%02d' % t.day # app::0::crash::2012-10-22 REDIS.hincrby key, group, 1 REDIS.hincrby key, 'ALL', 1 field << "-" << '%02d' % t.hour # app::0::crash::2012-10-22-00 REDIS.hincrby key, group, 1 REDIS.hincrby key, 'ALL', 1 end



MongoDB

> Account.first.id=> BSON::ObjectId('507db04798a3340ada000002')


Sequential ID Generation

34

ZADD key score member (>= 1.2) O(log(N)) > ZADD “sequential_ids::accounts” 10 507db04798a3340ada000002(integer) 1

sequential_ids::accounts { 10 5084bfbb98a33406f0000002, 9 5084bfa798a33406f0000001, 8 507db04798a3340ada000002, ...}



35


ZCARD key (>= 1.2) O(1) > ZCARD “sequential_ids::accounts”(integer) 9

ZADD key score member (>= 1.2) O(log(N)) > ZADD “sequential_ids::accounts” 10 5084bfbb98a33406f0000002(integer) 1



36


ZSCORE key member (>= 1.2) O(1) > ZSCORE “sequential_ids::accounts” 5084bfbb98a33406f0000002(integer) 10



37

def sequential_id(obj) key = "sequential_keys::#{obj.class.name.downcase.pluralize}" id = obj.id.to_s

# Lua script to atomically determine the score of an id. # If needed, adds it to the set with the next available score. # In the general case, O(1). On add, O(log(N)). Requires Redis >= 2.6 monotonic_zadd = <<LUA local sequential_id = redis.call('zscore', KEYS[1], ARGV[1]) if not sequential_id then sequential_id = redis.call('zcard', KEYS[1]) redis.call('zadd', KEYS[1], sequential_id, ARGV[1]) end

return sequential_idLUA

REDIS.eval(monotonic_zadd, [key], [id]).to_iend


Redis Analytics Wish List


‣ MSETBIT, MGETBIT, MBITCOUNT, HMINCRBY• Can already be addressed with scripting

‣ Native support for (insertion-)ordered sets‣ Per-hash-key expiration policies

Redis Analytics Wish List


Q&A

40

@JeffSeibertCEO, Crashlytics

scaling crashlytics: building analytics on redis 2.6

Technology

active user tracking

active users

users active

user x active

getbit accounts

key bitcount accounts

accounts integer

w41 bitcount accounts