For this upcoming meetup Juan Valencia, Principal Engineer at ShareThis, will be presenting on their real-world use of Apache Cassandra for high throughput and mission critical applications. This meetup will cover how to set up your projects successfully by having a good data model, running Cassandra, and using the Hector Java client. We will have a Q&A session at the end of Juan's presentation, to ensure everyone's questions are answered. Hope you can make it! What You Will Learn at this Meetup: • Real-World Use Case on ShareThis + Apache Cassandra • Data Modeling with Apache Cassandra • Using the Java Hector Client Library with Cassandra Abstract Juan Valencia, Principal Engineer at ShareThis, will be presenting on the use of Cassandra for high throughput applications. ShareThis has been running on Cassandra since version 0.6 and currently runs 4 Cassandra clusters, powering batch analytics, real-time analytics, a counter service, and a data lookup service.

1. Real-World Cassandra at ShareThis
Use Cases, Data Modeling, and Hector
1

2. ShareThis + Our Customers: Keys to Unlocking Social
1. DEPLOY SOCIAL TOOLS ACROSS BRANDS (AND DEVICES)
2. TAKE YOUR SOCIAL INVENTORY TO MARKET
3. LEVERAGE SHARETHIS: FOR DIRECT SALES, RESEARCH AND UN-RESERVED INVENTORY
2

3. Largest Ecosystem For Sharing and Engagement Across The Web
120 SOCIAL CHANNELS
SHARETHIS ECOSYSTEM
211 MILLION PEOPLE
(95.1% of the web)
2.4 MILLION PUBLISHERS
Source: ComScore U.S. January 2013; internal numbers, January 2013
3

4. Data Modeling and Why it Matters (Keep it even, Keep it slice-able)

5. Use Cases
5

6. A New Product: SnapSets
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7. Use Case: SnapSets, A New Product

8. Use Case: SnapSets, A New Product (Continued)
CF: Users (userId)
meta:first_name=Ronald
meta:last_name=Melencio
meta:username=ronsharethis
scrapbook:timestamp:scrapbookId:name=Scrapbook 1
scrapbook:timestamp:scrapbookId:date_created=Jan 10
url1:sid:clipID={LOCATION DATA}
url1:sid:456={LOCATION DATA}
CF: Scrapbooks (scrapbookId)
clip:timestamp:clipId:url=sharethis.com
clip:timestamp:clipId:title=Clip 1
clip:timestamp:clipId:likes=10
CF: Clip (clipId)
comment:timestamp:commentId={"name":"Ronald","timestamp":'"jan 10","comment":"hi"}
CF: Stats (user:userId,application,publisher:pubId)
meta:total_scrapbooks=1
meta:total_clips=100
meta:total_scrapbook_comments=100
scrapbook:timestamp:scrapbookId:total_comments=10
scrapbook:timestamp:scrapbookId:clip:timestamp:clipId:likes=10
scrapbook:timestamp:scrapbookId:clip:timestamp:clipId:dislikes=10

9. Use Cases
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10. High Velocity Reads and Writes: Count Service
9 – hi1.4xlarge
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11. Use Case: Count Service for URL's
●
1 Billion Pageviews per day = 12k pageviews per second
●
60 Million Social Referrals per day = 720 social referrals per second
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1 Million Shares per day = 12 shares per second
●
No expiration of Data* (3bn rows)
●
Requires minimum latency possible
●
Multiple read requests per page on blogs
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Normalize and Hash the URL for a row key
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Each social channel is a column
●
Retrieve the whole row for counts
●
Fix it by cheating ^_^ *

12. Use Cases
12

13. Insights that Matter – Your Social Analytics Dashboard
Timely Social Analytics
Benchmark your social
engagement with SQI
Identify
popular articles
Dive deeper into your
most social content
Measure social
activity on an hourly,
daily, weekly &
monthly basis.
Uncover which social
channels are driving
the most social traffic
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14. Use Case: Loading Processed Batch Data
●
Backend Hadoop stack for processing analytics
●
58 JSON schemas map tabular data to key/value storage for slicing
●
MondoDB* did not scale for frequent row level writes on the same table
●
Needed to maintain read throughput during spikes to writes when
analytics were finished
●
No TTL* - Works daily, doesn't work hourly
●
Switching from Astyanax to Hector
●
Using a Hector Client through Java API's

15. Use Case: Loading Processed Batch Data (continued)
{
}
"schema":
[
{
"column_name":"publisher",
"column_type":"UTF8Type",
"column_level":"common",
"column_master":""
},
{"column_name":"domain","column_type":"UTF8Type","column_level":"common","column_master":""},
{"column_name":"percenta","column_type":"FloatType","column_level":"composite_slave","column_master":"category"},
{"column_name":"percentb","column_type":"FloatType","column_level":"composite_slave","column_master":"category"},
{"column_name":"sqi","column_type":"FloatType","column_level":"composite_slave","column_master":"category"},
{"column_name":"month","column_type":"UTF8Type","column_level":"partition","column_master":""},
{"column_name":"category","column_type":"UTF8Type","column_level":"composite_master","column_master":""}
],
"row_key_format": "publisher:domain:month",
"column_family_name": "sqi_table"
CF -> Data Type
Row -> Publisher:domain:timestamp
Columns -> master:slave = value (topics, categories, urls, timestamps, etc)

16. Use Cases
16

17. Insights that Matter – Your Social Analytics Dashboard
Real Time Social Analytics
Benchmark your social
engagement with SQI
Identify trending
articles in real-time
Dive deeper into your
most social content
Measure social
activity on an hourly,
daily, weekly &
monthly basis.
Uncover which social
channels are driving
the most social traffic
12 cc1.4xlarge
17

18. Insights that Matter – Your Social Analytics Dashboard
Real Time Social Analytics
Benchmark your social
engagement with SQI
Identify trending
articles in real-time
Dive deeper into your
most social content
Measure social
activity on an hourly,
daily, weekly &
monthly basis.
Uncover which social
channels are driving
the most social traffic
12 cc1.4xlarge
18

19. Insights that Matter – And aren't accessible

20. Insights that Matter – And aren't accessible

21. Insights that Matter – And aren't accessible
●
Too many columns – unbounded url / channel sets
●
Cascading failure
●
Solutions:
–
Bigger Boxes – meh...
–
Split up the columns – split the rowkeys
●
–
Split up the columns – split the CF
●
–
Hash Urls and keep stats separate
Move URLs to their own space
Split up the columns – split the Keyspace
●
Keyspace is a timestamp

22. Ask Good
Data Modeling
Questions
22

23. ●
●
●
●
●
●
●
How many rows will there be?
How many columns per row will you need?
How will you slice your data?
What are the maximum number of rows ?
What are the maximum number of columns?
Is your data relational?
How long will your data live?
23

24. Hector
https://github.com/hector-client/hector/wiki/User-Guide
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25. Hector Imports
import me.prettyprint.cassandra.model.BasicColumnFamilyDefinition;
import me.prettyprint.cassandra.model.ConfigurableConsistencyLevel;
import me.prettyprint.cassandra.serializers.LongSerializer;
import me.prettyprint.cassandra.serializers.StringSerializer;
import me.prettyprint.cassandra.service.ColumnSliceIterator;
import me.prettyprint.cassandra.service.ThriftCfDef;
import me.prettyprint.cassandra.service.ThriftKsDef;
import me.prettyprint.cassandra.service.template.ColumnFamilyResult;
import me.prettyprint.cassandra.service.template.ColumnFamilyTemplate;
import me.prettyprint.cassandra.service.template.ThriftColumnFamilyTemplate;
import me.prettyprint.hector.api.beans.ColumnSlice;
import me.prettyprint.hector.api.beans.HColumn;
import me.prettyprint.hector.api.beans.HCounterColumn;
import me.prettyprint.hector.api.ddl.ColumnFamilyDefinition;
import me.prettyprint.hector.api.ddl.ComparatorType;
import me.prettyprint.hector.api.ddl.KeyspaceDefinition;
import me.prettyprint.hector.api.exceptions.HectorException;
import me.prettyprint.hector.api.factory.HFactory;
import me.prettyprint.hector.api.mutation.Mutator;
import me.prettyprint.hector.api.query.ColumnQuery;
import me.prettyprint.hector.api.query.CounterQuery;
import me.prettyprint.hector.api.query.QueryResult;
import me.prettyprint.hector.api.query.SliceCounterQuery;
import me.prettyprint.hector.api.query.SliceQuery;

26. Hector: Add a keyspace
public static Cluster getCluster(String name, String hosts) {
return HFactory.getOrCreateCluster(name, hosts);
}
public static KeyspaceDefinition createKeyspaceDefinition(String keyspaceName, int replication) {
return HFactory.createKeyspaceDefinition(
keyspaceName,
ThriftKsDef.DEF_STRATEGY_CLASS, // "org.apache.cassandra.locator.SimpleStrategy"
replication,
null // ArrayList of CF definitions
);
}
public static void addKeyspace(Cluster cluster, KeyspaceDefinition ksDef) {
KeyspaceDefinition keyspaceDef = cluster.describeKeyspace(ksDef.getName());
if (keyspaceDef == null) {
cluster.addKeyspace(ksDef, true);
System.out.println("Created keyspace: " + ksDef.getName());
} else {
System.err.println("Keyspace already exists");
}
}

27. Hector: Define a CF
public static ColumnFamilyDefinition createGenericColumnFamilyDefinition(
String ksName, String cfName, ComparatorType ctName) {
BasicColumnFamilyDefinition columnFamilyDefinition = new BasicColumnFamilyDefinition();
columnFamilyDefinition.setKeyspaceName(ksName);
columnFamilyDefinition.setName(cfName);
columnFamilyDefinition.setDefaultValidationClass(ctName.getClassName());
columnFamilyDefinition.setReplicateOnWrite(true);
return new ThriftCfDef(columnFamilyDefinition);
}
public static ColumnFamilyDefinition createCounterColumnFamilyDefinition(String ksName, String cfName) {
BasicColumnFamilyDefinition columnFamilyDefinition = new BasicColumnFamilyDefinition();
columnFamilyDefinition.setKeyspaceName(ksName);
columnFamilyDefinition.setName(cfName);
columnFamilyDefinition.setDefaultValidationClass(ComparatorType.COUNTERTYPE.getClassName());
columnFamilyDefinition.setReplicateOnWrite(true);
return new ThriftCfDef(columnFamilyDefinition);
}

28. Hector: Add a CF
Keyspace k = HFactory.createKeyspace(nameString, cluster);
public static void addColumnFamily(Cluster cluster, Keyspace keyspace, ColumnFamilyDefinition cfDef) {
KeyspaceDefinition ksDef = cluster.describeKeyspace(keyspace.getKeyspaceName());
if (ksDef != null) {
List<ColumnFamilyDefinition> list = ksDef.getCfDefs();
String cfName = cfDef.getName();
boolean exists = false;
for (ColumnFamilyDefinition myCfDef : list) {
if (myCfDef.getName().equals(cfName)) {
exists = true;
System.err.println("Found Column Family: " + cfName + ". Did not insert.");
}
}
if (!exists) {
cluster.addColumnFamily(cfDef, true);
System.out.println("Created column family: " + cfDef.getName());
}
} else {
System.err.println("Keyspace definition is null");
}
}

29. Hector: Insert Column
public static void insertColumn(
Cluster cluster, Keyspace keyspace,
String cfName, String rowKey,
String columnName, String columnValue) {
Mutator<String> mutator = HFactory.createMutator(keyspace, StringSerializer.get());
//HFactory.createColumn(columnName, columnValue, StringSerializer.get(), StringSerializer.get())
HColumn<String, String> hCol = HFactory.createStringColumn(columnName, columnValue);
mutator.insert(rowKey, cfName, hCol);
mutator.execute();
}
public static void incrementCounter(
Cluster cluster, Keyspace keyspace,
String cfName, String rowKey,
String counterColumnName) {
Mutator<String> mutator = HFactory.createMutator(keyspace, StringSerializer.get());
mutator.insertCounter(
rowKey, cfName, HFactory.createCounterColumn(counterColumnName, 1, StringSerializer.get()));
mutator.execute();
}

30. Hector: Read Column
public static String getColumn(
Cluster cluster, Keyspace keyspace,
String cfName, String rowKey,
String columnName) {
ColumnQuery<String, String, String> query =
Hfactory.createColumnQuery(
keyspace, StringSerializer.get(), StringSerializer.get(), StringSerializer.get());
query.setColumnFamily(cfName).setKey(rowKey).setName(columnName);
HColumn<String, String> value = query.execute().get();
if (value != null) {
return value.getValue();
}
return "";
}

31. Hector: Read Column
public static String getColumn(
Cluster cluster, Keyspace keyspace,
String cfName, String rowKey,
String columnName) {
ColumnQuery<String, String, String> query =
Hfactory.createColumnQuery(
keyspace, StringSerializer.get(), StringSerializer.get(), StringSerializer.get());
query.setColumnFamily(cfName).setKey(rowKey).setName(columnName);
HColumn<String, String> value = query.execute().get();
if (value != null) {
return value.getValue();
}
return "";
}

32. Hector: Read Column
public static long getCounter(
Cluster cluster, Keyspace keyspace,
String cfName, String rowKey,
String counterColumnName) {
CounterQuery<String, String> query =
HFactory.createCounterColumnQuery(keyspace, StringSerializer.get(),StringSerializer.get());
}
query.setColumnFamily(cfName).setKey(rowKey).setName(counterColumnName);
HCounterColumn<String> counter = query.execute().get();
if (counter != null) {
return counter.getValue();
}
return 0;

33. Hector: Read A Slice
public static Map<String, String> getSlice(
Cluster cluster, Keyspace keyspace,
String cfName, String rowKey,
String start, String end,
boolean reversed, int count) {
SliceQuery<String, String, String> query =
HFactory.createSliceQuery(keyspace, StringSerializer.get(), StringSerializer.get(), StringSerializer.get());
// for counter use HFactory.createSliceQuery
query.setColumnFamily(cfName);
query.setKey(rowKey);
query.setRange(start, end, reversed, count);
Iterator<HColumn<String, String>> iter = query.execute().get().getColumns().iterator();
Map<String, String> answer = new HashMap<String, String>();
while (iter.hasNext()) {
HColumn<String, String> temp = iter.next();
answer.put(temp.getName(), temp.getValue());
}
return answer;
}

34. Hector: Read All Columns
public static Map<String, String> getAllValues(
Cluster cluster, String keyspace,
String cf, String rowkey) {
HashMap<String, String> values = new HashMap<String, String>();
Keyspace keyspaceObject = HFactory.createKeyspace(keyspace, cluster);
SliceQuery<String,String,String> query =
Hfactory.createSliceQuery(
keyspaceObject, StringSerializer.get(), StringSerializer.get(), StringSerializer.get());
query.setColumnFamily(cf).setKey(rowkey).setRange("", "", true, 10000);
QueryResult<ColumnSlice<String,String>> result = query.execute();
Iterator<HColumn<String, String>> iter = result.get().getColumns().iterator();
while (iter.hasNext()) {
HColumn<String, String> current = iter.next();
values.put(current.getName(), current.getValue());
}
return values;
}

35. Hector: DANGER
private static void dropAllKeyspaces(Cluster cluster) {
for (KeyspaceDefinition ksDef: cluster.describeKeyspaces()) {
if (!(ksDef.getName().equals("system") || ksDef.getName().equals("OpsCenter"))) {
cluster.dropKeyspace(ksDef.getName(), true);
System.out.println("Dropped keyspace: " + ksDef.getName());
}
}
}
private static void dropKeyspace(Cluster cluster, String keyspace) {
KeyspaceDefinition ksDef = createKeyspaceDefinition(keyspace, Hector.replication);
cluster.dropKeyspace(ksDef.getName(), true);
System.out.println("Dropped keyspace: " + ksDef.getName());
}
private static void dropColumnFamily(Cluster cluster, String keyspace, String cf) {
cluster.dropColumnFamily(keyspace, cf);
System.out.println("Dropped Column Family: " + cf );
}

36. Conclusions
●
Data Modeling is Important
●
Use Cassandra for write throughput
●
Keep your ring even and your data slice-able
●
Wrap your libraries and switch when you need to

37. We're hiring: http://www.sharethis.com/about/careers
●
●
●
Work with REAL big data, billions of requests per day
Work on products that millions people see and interact with on a daily
basis
●
Work with a real-time pipeline, machine learning, complex user models
●
#1 fastest growing company San Francisco
●
free lunches
●
... and of course work with a bunch fun, smart people and PhDs

38. Thank You!
38