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Real-World Cassandra at ShareThis

Use Cases, Data Modeling, and Hector

ShareThis + Our Customers: Keys to Unlocking Social

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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

Largest Ecosystem For Sharing and EngagementEngagement Across The Web

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120 SOCIAL CHANNELS120 SOCIAL CHANNELS

SHARETHIS ECOSYSTEMSHARETHIS ECOSYSTEM

211 MILLION PEOPLE(95.1% of the web)

2.4 MILLION PUBLISHERS

Source: ComScore U.S. January 2013; internal numbers, January 2013

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

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Use Cases

A New Product: SnapSets

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A New Product: SnapSets

Use Case: SnapSets, A New Product

Use Case: SnapSets, A New Product (Continued)

CF: Users (userId)meta:first_name=Ronaldmeta:last_name=Melenciometa:username=ronsharethisscrapbook:timestamp:scrapbookId:name=Scrapbook 1scrapbook:timestamp:scrapbookId:date_created=Jan 10url1:sid:clipID={LOCATION DATA}url1:sid:456={LOCATION DATA}

CF: Scrapbooks (scrapbookId)clip:timestamp:clipId:url=sharethis.comclip:timestamp:clipId:title=Clip 1clip: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=1meta:total_clips=100meta:total_scrapbook_comments=100scrapbook:timestamp:scrapbookId:total_comments=10scrapbook:timestamp:scrapbookId:clip:timestamp:clipId:likes=10scrapbook:timestamp:scrapbookId:clip:timestamp:clipId:dislikes=10

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Use Cases

High Velocity Reads and Writes: Count Service

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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

● 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

● Normalize and Hash the URL for a row key

● Each social channel is a column

● Retrieve the whole row for counts

● Fix it by cheating ^_^ *

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Use Cases

Insights that Matter – Your Social Analytics Dashboard

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Timely Social Analytics

Dive deeper into your most social content

Identify popular articles

Uncover which social channels are driving

the most social traffic

Benchmark your social engagement with SQI

Measure social activity on an hourly, daily, weekly & monthly basis.

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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

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 TypeRow -> Publisher:domain:timestampColumns -> master:slave = value (topics, categories, urls, timestamps, etc)

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Use Cases

Insights that Matter – Your Social Analytics Dashboard

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Real Time Social Analytics

Dive deeper into your most social content

Identify trending articles in real-time

Uncover which social channels are driving

the most social traffic

Benchmark your social engagement with SQI

Measure social activity on an hourly, daily, weekly & monthly basis.

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Insights that Matter – Your Social Analytics Dashboard

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Real Time Social Analytics

Dive deeper into your most social content

Identify trending articles in real-time

Uncover which social channels are driving

the most social traffic

Benchmark your social engagement with SQI

Measure social activity on an hourly, daily, weekly & monthly basis.

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Insights that Matter – And aren't accessible

Insights that Matter – And aren't accessible

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

● Hash Urls and keep stats separate

– Split up the columns – split the CF

● Move URLs to their own space

– Split up the columns – split the Keyspace

● Keyspace is a timestamp

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Ask Good Data Modeling Questions

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● 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?

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Hectorhttps://github.com/hector-client/hector/wiki/User-Guide

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;

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"); } }

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); }

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"); } }

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(); }

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 ""; }

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 ""; }

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; }

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; }

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; }

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 ); }

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

● 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

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Thank You!