2. Who is this guy
• Staff Engineer, Compute and Data Services, Ooyala
• Building multiple web-scale real-time systems on top of C*, Kafka,
Storm, etc.
• Scala/Akka guy
• Very excited by open source, big data projects - share some today
• @evanfchan
Tuesday, June 18, 13
5. Agenda
• Ooyala and Cassandra
• What problem are we trying to solve?
Tuesday, June 18, 13
6. Agenda
• Ooyala and Cassandra
• What problem are we trying to solve?
• Spark and Shark
Tuesday, June 18, 13
7. Agenda
• Ooyala and Cassandra
• What problem are we trying to solve?
• Spark and Shark
• Our Spark/Cassandra Architecture
Tuesday, June 18, 13
8. Agenda
• Ooyala and Cassandra
• What problem are we trying to solve?
• Spark and Shark
• Our Spark/Cassandra Architecture
• Demo
Tuesday, June 18, 13
11. CONFIDENTIAL—DO NOT DISTRIBUTE 6CONFIDENTIAL—DO NOT DISTRIBUTE
Founded in 2007
Commercially launch in 2009
230+ employees in Silicon Valley, LA, NYC,
London, Paris, Tokyo, Sydney & Guadalajara
Global footprint, 200M unique users,
110+ countries, and more than 6,000 websites
Over 1 billion videos played per month
and 2 billion analytic events per day
25% of U.S. online viewers watch video
powered by Ooyala
COMPANY OVERVIEW
Tuesday, June 18, 13
12. CONFIDENTIAL—DO NOT DISTRIBUTE 7
TRUSTED VIDEO PARTNER
STRATEGIC PARTNERS
CUSTOMERS
CONFIDENTIAL—DO NOT DISTRIBUTE
Tuesday, June 18, 13
13. We are a large Cassandra user
Tuesday, June 18, 13
14. We are a large Cassandra user
• 11 clusters ranging in size from 3 to 36 nodes
Tuesday, June 18, 13
15. We are a large Cassandra user
• 11 clusters ranging in size from 3 to 36 nodes
• Total of 28TB of data managed over ~85 nodes
Tuesday, June 18, 13
16. We are a large Cassandra user
• 11 clusters ranging in size from 3 to 36 nodes
• Total of 28TB of data managed over ~85 nodes
• Over 2 billion C* column writes per day
Tuesday, June 18, 13
17. We are a large Cassandra user
• 11 clusters ranging in size from 3 to 36 nodes
• Total of 28TB of data managed over ~85 nodes
• Over 2 billion C* column writes per day
• Powers all of our analytics infrastructure
Tuesday, June 18, 13
18. We are a large Cassandra user
• 11 clusters ranging in size from 3 to 36 nodes
• Total of 28TB of data managed over ~85 nodes
• Over 2 billion C* column writes per day
• Powers all of our analytics infrastructure
• Much much bigger cluster coming soon
Tuesday, June 18, 13
19. What problem are we trying to
solve?
Lots of data, complex queries, answered really quickly... but how??
Tuesday, June 18, 13
27. Today: Precomputed aggregates
• Video metrics computed along several high cardinality dimensions
• Very fast lookups, but inflexible, and hard to change
Tuesday, June 18, 13
28. Today: Precomputed aggregates
• Video metrics computed along several high cardinality dimensions
• Very fast lookups, but inflexible, and hard to change
• Most computed aggregates are never read
Tuesday, June 18, 13
29. Today: Precomputed aggregates
• Video metrics computed along several high cardinality dimensions
• Very fast lookups, but inflexible, and hard to change
• Most computed aggregates are never read
• What if we need more dynamic queries?
– Top content for mobile users in France
– Engagement curves for users who watched recommendations
– Data mining, trends, machine learning
Tuesday, June 18, 13
30. The static - dynamic continuum
• Super fast lookups
• Inflexible, wasteful
• Best for 80% most
common queries
• Always compute results
from raw data
• Flexible but slow
100% Precomputation 100% Dynamic
Tuesday, June 18, 13
31. The static - dynamic continuum
• Super fast lookups
• Inflexible, wasteful
• Best for 80% most
common queries
• Always compute results
from raw data
• Flexible but slow
100% Precomputation100% Dynamic
Tuesday, June 18, 13
32. Where we want to be
Partly dynamic
• Pre-aggregate most
common queries
• Flexible, fast dynamic
queries
• Easily generate many
materialized views
Tuesday, June 18, 13
41. Introduction to Spark
• In-memory distributed computing framework
• Created by UC Berkeley AMP Lab in 2010
Tuesday, June 18, 13
42. Introduction to Spark
• In-memory distributed computing framework
• Created by UC Berkeley AMP Lab in 2010
• Targeted problems that MR is bad at:
– Iterative algorithms (machine learning)
– Interactive data mining
Tuesday, June 18, 13
43. Introduction to Spark
• In-memory distributed computing framework
• Created by UC Berkeley AMP Lab in 2010
• Targeted problems that MR is bad at:
– Iterative algorithms (machine learning)
– Interactive data mining
• More general purpose than Hadoop MR
Tuesday, June 18, 13
44. Introduction to Spark
• In-memory distributed computing framework
• Created by UC Berkeley AMP Lab in 2010
• Targeted problems that MR is bad at:
– Iterative algorithms (machine learning)
– Interactive data mining
• More general purpose than Hadoop MR
• Active contributions from ~ 15 companies
Tuesday, June 18, 13
56. Developers love it
• “I wrote my first aggregation job in 30 minutes”
Tuesday, June 18, 13
57. Developers love it
• “I wrote my first aggregation job in 30 minutes”
• High level “distributed collections” API
Tuesday, June 18, 13
58. Developers love it
• “I wrote my first aggregation job in 30 minutes”
• High level “distributed collections” API
• No Hadoop cruft
Tuesday, June 18, 13
59. Developers love it
• “I wrote my first aggregation job in 30 minutes”
• High level “distributed collections” API
• No Hadoop cruft
• Full power of Scala, Java, Python
Tuesday, June 18, 13
60. Developers love it
• “I wrote my first aggregation job in 30 minutes”
• High level “distributed collections” API
• No Hadoop cruft
• Full power of Scala, Java, Python
• Interactive REPL shell
Tuesday, June 18, 13
61. Developers love it
• “I wrote my first aggregation job in 30 minutes”
• High level “distributed collections” API
• No Hadoop cruft
• Full power of Scala, Java, Python
• Interactive REPL shell
• EASY testing!!
Tuesday, June 18, 13
62. Developers love it
• “I wrote my first aggregation job in 30 minutes”
• High level “distributed collections” API
• No Hadoop cruft
• Full power of Scala, Java, Python
• Interactive REPL shell
• EASY testing!!
• Low latency - quick development cycles
Tuesday, June 18, 13
63. Spark word count example
1 package org.myorg;
2
3 import java.io.IOException;
4 import java.util.*;
5
6 import org.apache.hadoop.fs.Path;
7 import org.apache.hadoop.conf.*;
8 import org.apache.hadoop.io.*;
9 import org.apache.hadoop.mapreduce.*;
10 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
11 import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
12 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
13 import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
14
15 public class WordCount {
16
17 public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
18 private final static IntWritable one = new IntWritable(1);
19 private Text word = new Text();
20
21 public void map(LongWritable key, Text value, Context context) throws IOException,
InterruptedException {
22 String line = value.toString();
23 StringTokenizer tokenizer = new StringTokenizer(line);
24 while (tokenizer.hasMoreTokens()) {
25 word.set(tokenizer.nextToken());
26 context.write(word, one);
27 }
28 }
29 }
30
31 public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
32
33 public void reduce(Text key, Iterable<IntWritable> values, Context context)
34 throws IOException, InterruptedException {
35 int sum = 0;
36 for (IntWritable val : values) {
37 sum += val.get();
38 }
39 context.write(key, new IntWritable(sum));
40 }
41 }
42
43 public static void main(String[] args) throws Exception {
44 Configuration conf = new Configuration();
45
46 Job job = new Job(conf, "wordcount");
47
48 job.setOutputKeyClass(Text.class);
49 job.setOutputValueClass(IntWritable.class);
50
51 job.setMapperClass(Map.class);
52 job.setReducerClass(Reduce.class);
53
54 job.setInputFormatClass(TextInputFormat.class);
55 job.setOutputFormatClass(TextOutputFormat.class);
56
57 FileInputFormat.addInputPath(job, new Path(args[0]));
58 FileOutputFormat.setOutputPath(job, new Path(args[1]));
59
60 job.waitForCompletion(true);
61 }
62
63 }
Tuesday, June 18, 13
64. Spark word count example
file = spark.textFile("hdfs://...")
file.flatMap(line => line.split(" "))
.map(word => (word, 1))
.reduceByKey(_ + _)
1 package org.myorg;
2
3 import java.io.IOException;
4 import java.util.*;
5
6 import org.apache.hadoop.fs.Path;
7 import org.apache.hadoop.conf.*;
8 import org.apache.hadoop.io.*;
9 import org.apache.hadoop.mapreduce.*;
10 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
11 import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
12 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
13 import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
14
15 public class WordCount {
16
17 public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
18 private final static IntWritable one = new IntWritable(1);
19 private Text word = new Text();
20
21 public void map(LongWritable key, Text value, Context context) throws IOException,
InterruptedException {
22 String line = value.toString();
23 StringTokenizer tokenizer = new StringTokenizer(line);
24 while (tokenizer.hasMoreTokens()) {
25 word.set(tokenizer.nextToken());
26 context.write(word, one);
27 }
28 }
29 }
30
31 public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
32
33 public void reduce(Text key, Iterable<IntWritable> values, Context context)
34 throws IOException, InterruptedException {
35 int sum = 0;
36 for (IntWritable val : values) {
37 sum += val.get();
38 }
39 context.write(key, new IntWritable(sum));
40 }
41 }
42
43 public static void main(String[] args) throws Exception {
44 Configuration conf = new Configuration();
45
46 Job job = new Job(conf, "wordcount");
47
48 job.setOutputKeyClass(Text.class);
49 job.setOutputValueClass(IntWritable.class);
50
51 job.setMapperClass(Map.class);
52 job.setReducerClass(Reduce.class);
53
54 job.setInputFormatClass(TextInputFormat.class);
55 job.setOutputFormatClass(TextOutputFormat.class);
56
57 FileInputFormat.addInputPath(job, new Path(args[0]));
58 FileOutputFormat.setOutputPath(job, new Path(args[1]));
59
60 job.waitForCompletion(true);
61 }
62
63 }
Tuesday, June 18, 13
70. Shark - HIVE on Spark
• 100% HiveQL compatible
Tuesday, June 18, 13
71. Shark - HIVE on Spark
• 100% HiveQL compatible
• 10-100x faster than HIVE, answers in seconds
Tuesday, June 18, 13
72. Shark - HIVE on Spark
• 100% HiveQL compatible
• 10-100x faster than HIVE, answers in seconds
• Reuse UDFs, SerDe’s, StorageHandlers
Tuesday, June 18, 13
73. Shark - HIVE on Spark
• 100% HiveQL compatible
• 10-100x faster than HIVE, answers in seconds
• Reuse UDFs, SerDe’s, StorageHandlers
• Can use DSE / CassandraFS for Metastore
Tuesday, June 18, 13
74. Shark - HIVE on Spark
• 100% HiveQL compatible
• 10-100x faster than HIVE, answers in seconds
• Reuse UDFs, SerDe’s, StorageHandlers
• Can use DSE / CassandraFS for Metastore
• Easy Scala/Java integration via Spark - easier than
writing UDFs
Tuesday, June 18, 13
75. Our new analytics architecture
How we integrate Cassandra and Spark/Shark
Tuesday, June 18, 13
76. From raw events to fast queries
Raw
Events
Raw
Events
Raw
Events
Tuesday, June 18, 13
77. From raw events to fast queries
Ingestion
C*
event
store
Raw
Events
Raw
Events
Raw
Events
Tuesday, June 18, 13
78. From raw events to fast queries
Ingestion
C*
event
store
Raw
Events
Raw
Events
Raw
Events
Spark
Spark
Spark
View 1
View 2
View 3
Tuesday, June 18, 13
79. From raw events to fast queries
Ingestion
C*
event
store
Raw
Events
Raw
Events
Raw
Events
Spark
Spark
Spark
View 1
View 2
View 3
Spark
Predefined
queries
Tuesday, June 18, 13
80. From raw events to fast queries
Ingestion
C*
event
store
Raw
Events
Raw
Events
Raw
Events
Spark
Spark
Spark
View 1
View 2
View 3
Spark
Shark
Predefined
queries
Ad-hoc
HiveQL
Tuesday, June 18, 13
93. Tips for InputFormat Development
• Know which target platforms you are developing for
– Which API to write against? New? Old? Both?
Tuesday, June 18, 13
94. Tips for InputFormat Development
• Know which target platforms you are developing for
– Which API to write against? New? Old? Both?
• Be prepared to spend time tuning your split computation
– Low latency jobs require fast splits
Tuesday, June 18, 13
95. Tips for InputFormat Development
• Know which target platforms you are developing for
– Which API to write against? New? Old? Both?
• Be prepared to spend time tuning your split computation
– Low latency jobs require fast splits
• Consider sorting row keys by token for data locality
Tuesday, June 18, 13
96. Tips for InputFormat Development
• Know which target platforms you are developing for
– Which API to write against? New? Old? Both?
• Be prepared to spend time tuning your split computation
– Low latency jobs require fast splits
• Consider sorting row keys by token for data locality
• Implement predicate pushdown for HIVE SerDe’s
– Use your indexes to reduce size of dataset
Tuesday, June 18, 13
113. Fault Tolerance
• Cached dataset lives in Java Heap only - what if process dies?
Tuesday, June 18, 13
114. Fault Tolerance
• Cached dataset lives in Java Heap only - what if process dies?
• Spark lineage - automatic recomputation from source, but this is
expensive!
Tuesday, June 18, 13
115. Fault Tolerance
• Cached dataset lives in Java Heap only - what if process dies?
• Spark lineage - automatic recomputation from source, but this is
expensive!
• Can also replicate cached dataset to survive single node failures
Tuesday, June 18, 13
116. Fault Tolerance
• Cached dataset lives in Java Heap only - what if process dies?
• Spark lineage - automatic recomputation from source, but this is
expensive!
• Can also replicate cached dataset to survive single node failures
• Persist materialized views back to C*, then load into cache -- now
recovery path is much faster
Tuesday, June 18, 13
117. Fault Tolerance
• Cached dataset lives in Java Heap only - what if process dies?
• Spark lineage - automatic recomputation from source, but this is
expensive!
• Can also replicate cached dataset to survive single node failures
• Persist materialized views back to C*, then load into cache -- now
recovery path is much faster
• Persistence also enables multiple processes to hold cached dataset
Tuesday, June 18, 13
119. Shark Demo
• Local shark node, 1 core, MBP
• How to create a table from C* using our inputformat
• Creating a cached Shark table
• Running fast queries
Tuesday, June 18, 13
128. Spark: Under the hood
Map DatasetReduce Map
Driver Map DatasetReduce Map
Map DatasetReduce Map
One executor process per node
Driver
Tuesday, June 18, 13