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Realtime Analytics
 with Cassandra
   or: How I Learned to
  Stopped Worrying and
       Love Counting


                          1
What is Realtime Analytics?
    eg “show me the number of mentions of
        ‘Acunu’ per day, between May and
          November 2011, on Twitter”


       Batch (Hadoop) approach would
    require processing ~30 billion tweets,
              or ~4.2 TB of data
                 http://blog.twitter.com/2011/03/numbers.html




                                                                2
Introduction



    Live & historical
      aggregates...




3

                                       3
Realtime trends...




4

                         4
Drill downs
    and roll ups


5

                   5
Okay, so how are we
     going to do it?
For each tweet,
increment a bunch of counters,
such that answering a query
is as easy as reading some counters

                                      6
Preparing the data
                              12:32:15 I like #trafficlights
Step 1: Get a feed of    12:33:43 Nobody expects...
        the tweets     12:33:49 I ate a #bee; woe is...
                      12:34:04 Man, @acunu rocks!

Step 2: Tokenise the
        tweet

Step 3: Increment counters            [1234, man]   +1
        in time buckets for           [1234, acunu] +1
        each token                    [1234, rock] +1

                                                              7
Querying
                            start: [01/05/11, acunu]
Step 1: Do a range query    end:   [30/05/11, acunu]

                                       Key            #Mentions
                              [01/05/11 00:01, acunu]    3
Step 2: Result table          [01/05/11 00:02, acunu]    5
                                        ...              ...


                              90

Step 3: Plot pretty graph     45
                               0
                                   May Jun Jul Aug Sept Oct Nov


                                                                  8
Except it’s not that easy...
• Cassandra best practice is to use RandomPartitioner,
  so not possible to range queries on rows
• Could manually work out each row in range, do lots of
  point gets
  • This would suck - each query would be 100’s of random
    IOs on disk
• Need to use wide rows, range query is a column slice,
  each query ~1 IO - Denormalisation

                                                            9
So instead of this...
                              Key            #Mentions
                     [01/05/11 00:01, acunu]    3
                     [01/05/11 00:02, acunu]    5
                               ...              ...




                     We do this
                  Key           00:01       00:02        ...
            [01/05/11, acunu]     3           5          ...
            [02/05/11, acunu]    12           4          ...
                    ...           ...                    ...

Row key is ‘big’                     Column key is ‘small’
 time bucket                             time bucket
                                                               10
Demo
./painbird.py -u tom_wilkie




                              11
Now its your
  turn.....



               12
1. Get a twitter account - http://twitter.com

2. Get some Cassandra VMs - http://goo.gl/O9hkv

3. Cluster them up

4. Get the code - http://goo.gl

5. Implement the missing bits!

6. (Prizes for the ones that spot bugs!)


                                                  13
Get some Cassandra
       VMs


http://goo.gl/O9hkv


                      14
Cluster them up
• SSH in, set password (on both!)
• Check you can connect to the UI
• Use UI (click add host)




                                    15
Get the code
SSH into one of the VMs:
# curl https://acunu-
oss.s3.amazonaws.com/painbird.tar.gz
| tar zxf -
# curl -o pycassa.rpm https://acunu-
oss.s3.amazonaws.com/pycassa.rpm
# rpm -i pycassa.rpm
# cd release
# ./painbird.py -u tom_wilkie
                                       16
Implement the “core”

• In core.py
• def insert_tweet(cassandra, tweet):
• def do_query(cassandra, term, start, finish):


                                                 17
Check you data
-bash-3.2$ cassandra-cli
Connected to: "Test Cluster" on localhost/9160
Welcome to Cassandra CLI version 1.0.8.acunu2
Type 'help;' or '?' for help.
Type 'quit;' or 'exit;' to quit.

[default@unknown] use painbird;
Authenticated to keyspace: painbird
[default@painbird] list keywords;
Using default limit of 100
-------------------
RowKey: m-5-"woe
=> (counter=11, value=1)
                                             18
Extensions



             19
UI                        Painbird
• Pretty graphs           •   mentions of multiple
• Automatically               terms
  periodically update     •   sentiment analysis -
• Search multiple terms       http://www.nltk.org/




                                                     20

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Realtime Analytics on the Twitter Firehose with Cassandra

  • 1. Realtime Analytics with Cassandra or: How I Learned to Stopped Worrying and Love Counting 1
  • 2. What is Realtime Analytics? eg “show me the number of mentions of ‘Acunu’ per day, between May and November 2011, on Twitter” Batch (Hadoop) approach would require processing ~30 billion tweets, or ~4.2 TB of data http://blog.twitter.com/2011/03/numbers.html 2
  • 3. Introduction Live & historical aggregates... 3 3
  • 5. Drill downs and roll ups 5 5
  • 6. Okay, so how are we going to do it? For each tweet, increment a bunch of counters, such that answering a query is as easy as reading some counters 6
  • 7. Preparing the data 12:32:15 I like #trafficlights Step 1: Get a feed of 12:33:43 Nobody expects... the tweets 12:33:49 I ate a #bee; woe is... 12:34:04 Man, @acunu rocks! Step 2: Tokenise the tweet Step 3: Increment counters [1234, man] +1 in time buckets for [1234, acunu] +1 each token [1234, rock] +1 7
  • 8. Querying start: [01/05/11, acunu] Step 1: Do a range query end: [30/05/11, acunu] Key #Mentions [01/05/11 00:01, acunu] 3 Step 2: Result table [01/05/11 00:02, acunu] 5 ... ... 90 Step 3: Plot pretty graph 45 0 May Jun Jul Aug Sept Oct Nov 8
  • 9. Except it’s not that easy... • Cassandra best practice is to use RandomPartitioner, so not possible to range queries on rows • Could manually work out each row in range, do lots of point gets • This would suck - each query would be 100’s of random IOs on disk • Need to use wide rows, range query is a column slice, each query ~1 IO - Denormalisation 9
  • 10. So instead of this... Key #Mentions [01/05/11 00:01, acunu] 3 [01/05/11 00:02, acunu] 5 ... ... We do this Key 00:01 00:02 ... [01/05/11, acunu] 3 5 ... [02/05/11, acunu] 12 4 ... ... ... ... Row key is ‘big’ Column key is ‘small’ time bucket time bucket 10
  • 12. Now its your turn..... 12
  • 13. 1. Get a twitter account - http://twitter.com 2. Get some Cassandra VMs - http://goo.gl/O9hkv 3. Cluster them up 4. Get the code - http://goo.gl 5. Implement the missing bits! 6. (Prizes for the ones that spot bugs!) 13
  • 14. Get some Cassandra VMs http://goo.gl/O9hkv 14
  • 15. Cluster them up • SSH in, set password (on both!) • Check you can connect to the UI • Use UI (click add host) 15
  • 16. Get the code SSH into one of the VMs: # curl https://acunu- oss.s3.amazonaws.com/painbird.tar.gz | tar zxf - # curl -o pycassa.rpm https://acunu- oss.s3.amazonaws.com/pycassa.rpm # rpm -i pycassa.rpm # cd release # ./painbird.py -u tom_wilkie 16
  • 17. Implement the “core” • In core.py • def insert_tweet(cassandra, tweet): • def do_query(cassandra, term, start, finish): 17
  • 18. Check you data -bash-3.2$ cassandra-cli Connected to: "Test Cluster" on localhost/9160 Welcome to Cassandra CLI version 1.0.8.acunu2 Type 'help;' or '?' for help. Type 'quit;' or 'exit;' to quit. [default@unknown] use painbird; Authenticated to keyspace: painbird [default@painbird] list keywords; Using default limit of 100 ------------------- RowKey: m-5-"woe => (counter=11, value=1) 18
  • 20. UI Painbird • Pretty graphs • mentions of multiple • Automatically terms periodically update • sentiment analysis - • Search multiple terms http://www.nltk.org/ 20