SlideShare a Scribd company logo
1 of 50
Download to read offline
Cassandra Indexing Techniques

             Ed Anuff
         Founder, Usergrid
   Cassandra Summit SF July, 2011
Agenda
•  Background
•  Basics of Indexes
•  Native Secondary Indexes
•  "Wide rows" and CF-based Indexes
•  Inverted-indexes Using SuperColumns
•  Inverted-indexes Using Composite Columns
•  Q&A
Background
This presentation is based on:

•  What we learned over the last year in building a
   highly indexed system on Cassandra

•  Participation in the Hector client project

•  Common questions and issues posted to the
   Cassandra mailing lists
Brief History - Cassandra 0.6
•  No built-in secondary indexes
•  All indexes were custom-built, usually using
   super-columns
•  Pros
   –  Forced you to learn how Cassandra works

•  Cons
   –  Lots of work
   –  Super-columns proved a dead-end
Brief History - Cassandra 0.7
•  Built-in secondary indexes
•  New users flocked to these
•  Pros
   –  Easy to use, out of the box

•  Cons
   –  Deceptively similar to SQL indexes but not the same
   –  Reinforce data modeling that plays against Cassandra’s
      strengths
Present Day
•  New users can now get started with Cassandra
   without really understanding it (CQL, etc.)
•  Veteran users are using advanced techniques
   (Composites, etc.) that aren’t really documented
   anywhere*
•  New user panic mode when they try to go to the
   next level and suddenly find themselves in the
   deep end

*Actually, they are, Google is your friend…
Let’s try to bridge the gap…
A Quick Review

There are essentially two ways of finding rows:

              The Primary Index
                 (row keys)

              Alternate Indexes
              (everything else)
The “Primary Index”
•  The “primary index” is your row key*
•  Sometimes it’s meaningful (“natural id”):
Users = {
  "edanuff" : {
    email: "ed@anuff.com"
  }
}



*Yeah. No. But if it helps, yes.
The “Primary Index”
•  The “primary index” is your row key*
•  But usually it’s not:
Users = {
  "4e3c0423-aa84-11e0-a743-58b0356a4c0a" : {
    username: "edanuff",
    email: "ed@anuff.com"
  }
}


*Yeah. No. But if it helps, yes.
Get vs. Find
•  Using the row key is the best way to retrieve
   something if you’ve got a precise and
   immutable 1:1 mapping
•  If you find yourself ever planning to iterate
   over keys, you’re probably doing something
   wrong
   –  i.e. avoid the Order Preserving Partitioner*

•  Use alternate indexes to find (search) things
*Feel free to disregard, but don’t complain later
Alternate Indexes

Anything other than using the row key:
•  Native secondary indexes
•  Wide rows as lookup and grouping tables
•  Custom secondary indexes
Remember, there is no magic here…
Native Secondary Indexes
•  Easy to use
•  Look (deceptively) like SQL indexes, especially
   when used with CQL

CREATE INDEX ON Users(username);

SELECT * FROM Users WHERE
username="edanuff";
Under The Hood
•  Every index is stored as its own "hidden" CF
•  Nodes index the rows they store

•  When you issue a query, it gets sent to all
   nodes
•  Currently does equality operations, the range
   operations get performed by in memory by
   coordinator node
Some Limitations
•  Not recommended for high cardinality values (i.e.
   timestamps, birthdates, keywords, etc.)
•  Requires at least one equality comparison in a
   query – not great for less-than/greater-than/range
   queries
•  Unsorted - results are in token order, not query
   value order
•  Limited to search on data types Cassandra
   natively understands
I can’t live with those limitations,
      what are my options?

  Complain on the mailing list

  Switch to Mongo

  Build my indexes in my application
That sounds hard…

Actually, it’s not, but it does require
 us to revisit some of Cassandra’s
           unique features
Wide Rows
     “Why would a row need 2B columns”?
•  Basis of all indexing, organizing, and
   relationships in Cassandra
•  If your data model has no rows with over a
   hundred columns, you’re either doing
   something wrong or shouldn’t be using
   Cassandra*
*IMHO J
Conventional Rows As Records
Users = {
  "4e3c0423-…” : {
     username: "edanuff",
     email: "ed@anuff.com",
     … : …
  },
  "e5d61f2b-…” : {
     username: "jdoe",
     email: "john.doe@gmail.com",
     … : …
  }
}
Wide Row For Grouping
Departments = {
  "Engineering" : {
     "e5d61f2b-…" : null,
     "e5d61f2b-…" : null,   Column names
     "e66afd40-…" : null,   are keys of rows
     "e719962b-…" : null,   in “Users” CF
     "e78ece0f-…" : null,
     "e80a17ba-…" : null,
     … : …,
  }
}
Wide Row As A Simple Index
         Comparator = “UTF8Type”
Indexes = {
  "User_Keys_By_Last_Name" : {
     "adams"      : "e5d61f2b-…",
     "alden"      : "e80a17ba-…",
     "anderson" : "e5d61f2b-…",
     "davis"      : "e719962b-…",
     "doe"        : "e78ece0f-…",
     "franks"     : "e66afd40-…",
     … : …,
  }
}
Column Families As Indexes
•  CF column operations very fast
•  Column slices can be retrieved by range, are
   always sorted, can be reversed, etc.
•  If target key a TimeUUID you get both grouping
   and sort by timestamp
  –  Good for inboxes, feeds, logs, etc. (Twissandra)
•  Best option when you need to combine groups,
   sort, and search
  –  Search friends list, inbox, etc.
But, only works for 1:1
     What happens when I’ve got one to many?
Indexes = {
  "User_Keys_By_Last_Name" : {
     "adams"       : "e5d61f2b-…",
     "alden"       : "e80a17ba-…",
     "anderson" : "e5d61f2b-…",
✖!   "anderson" : "e719962b-…",
                                         Not Allowed

     "doe"         : "e78ece0f-…",
     "franks"      : "e66afd40-…",
     … : …,
  }
}
SuperColumns to the rescue?
Indexes = {
  "User_Keys_By_Last_Name" : {
     "adams" : {
        "e5d61f2b-…" : null
     },
     "anderson" : {
        "e5d61f2b-…" : null,
        "e66afd40-…" : null
     }
  }
}
Use with caution
•  Not officially deprecated, but not highly
   recommended either
•  Sorts only on the supercolumn, not subcolumn
•  Some performance issues
•  What if I want more nesting?
   –  Can subcolumns have subcolumns? NO!

•  Anecdotally, many projects have moved away
   from using supercolumns
So, let’s revisit regular CF’s
     What happens when I’ve got one to many?
Indexes = {
  "User_Keys_By_Last_Name" : {
     "adams"       : "e5d61f2b-…",
     "alden"       : "e80a17ba-…",
     "anderson" : "e5d61f2b-…",
✖!   "anderson" : "e719962b-…",
                                         Not Allowed

     "doe"         : "e78ece0f-…",
     "franks"      : "e66afd40-…",
     … : …,
  }
}
So, let’s revisit regular CF’s
    What if we could turn it back to one to one?
Indexes = {
  "User_Keys_By_Last_Name" : {
     {"adams", 1}            : "e5d…",
     {"alden", 1}            : "e80…",
     {"anderson", 1} : "e5f…",
✔!   {"anderson", 2} : "e71…",
                                              Allowed!!!

     {"doe", 1}              : "e78…",
     {"franks", 1}           : "e66…",
     … : …,
  }
}
Composite Column Names
Comparator = “CompositeType” or “DynamicCompositeType”

  {"anderson", 1, 1, 1, …} : "e5f…"


                                     1..N Components

    Build your column name out of one or more
   “component” values which can be of any of the
         columns types Cassandra supports
      (i.e. UTF8, IntegerType, UUIDType, etc.)
Composite Column Names
Comparator = “CompositeType” or “DynamicCompositeType”

  {"anderson", 1, 1, 1, …} : "e5f…"
  {"anderson", 1, 1, 2, …} : "e5f…"
  {"anderson", 1, 2, 1, …} : "e5f…"
  {"anderson", 1, 2, 2, …} : "e5f…"

       Sorts by component values using each
            component type’s sort order
    Retrieve using normal column slice technique
Two Types Of Composites
- column_families:
  - name: My_Composite_Index_CF
  - compare_with:
     CompositeType(UTF8Type, UUIDType)

 - name: My_Dynamic_Composite_Index_CF
 - compare_with:
    DynamicCompositeType(s=>UTF8Type, u=>UUIDType)


Main difference for use in indexes is whether you
need to create one CF per index vs one CF for
       all indexes with one row per index
Static Composites
- column_families:
  - name: My_Composite_Index_CF
  - compare_with:
     CompositeType(UTF8Type, UUIDType)


{"anuff", "e5f…"} : "e5f…"

                                    Fixed # and order
                                    defined in column
                                       configuration
Dynamic Composites
- column_families:
  - name: My_Dynamic_Composite_Index_CF
  - compare_with:
     DynamicCompositeType(s=>UTF8Type, u=>UUIDType)


{"anuff", "e5f…", "e5f…"} : "…"
                                           Any # and
                                            order of
                                            types at
                                            runtime
Typical Composite Index Entry

{<term 1>, …, <term N>, <key>, <ts>}


 <term 1…N> - terms to query on (i.e. last_name, first_name)

 <key>          - target row key

 <ts>           - unique timestamp, usually time-based UUID
How does this work?
•  Queries are easy
  –  regular column slice operations

•  Updates are harder
  –  Need to remove old value and insert the new
     value
  –  Uh oh, read before write??!!!
Example – Users By Location
•  We need 3 Column Families (not 2)
•  First 2 CF’s are obvious:
  –  Users
  –  Indexes

•  We also need a third CF:
  –  Users_Index_Entries
Users CF
          Comparator = “BytesType”

Users = {
  <user_key> : {
    "username" : "…",
    "location" : <location>,
    … : …,
  }
}
Indexes CF
        Comparator = “CompositeType”

Indexes = {
  "Users_By_Location" : {
    {<location>, <user_key>, <ts>} : …,
    … : …,
  }
}
Users Index Entries CF
        Comparator = “CompositeType”

Users_Index_Entries = {
  <user_key> : {
    {"location", <ts 1>}    :   <location 1>,
    {"location", <ts 2>}    :   <location 2>,
    {"location", <ts N>}    :   <location N>,
    {"last_name", <ts 1>}   :   "…",
    … : …,
  }
}
Users Index Entries CF
        Comparator = “CompositeType”

Users_Index_Entries = {
  <user_key> : {
    {"location", <ts 1>}    :   <location 1>,
    {"location", <ts 2>}    :   <location 2>,
    {"location", <ts N>}    :   <location N>,
    {"last_name", <ts 1>}   :   "…",
    … : …,
  }
}
Users Index Entries CF
        Comparator = “CompositeType”

Users_Index_Entries = {
  <user_key> : {
    {"location", <ts 1>}    :   <location 1>,
    {"location", <ts 2>}    :   <location 2>,
    {"location", <ts N>}    :   <location N>,
    {"last_name", <ts 1>}   :   "…",
    … : …,
  }
}
Updating The Index
•  We read previous index values from the
   Users_Index_Entries CF rather than the
   Users CF to deal with concurrency

•  Columns in Index CF and
   Users_Index_Entries CF are timestamped so
   no locking is needed for concurrent updates
Get Old Values For Column
SELECT {"location"}..{"location",*}
FROM Users_Index_Entries WHERE KEY = <user_key>;

BEGIN BATCH

DELETE {"location", ts1}, {"location", ts2}, …
FROM Users_Index_Entries WHERE KEY = <user_key>;

DELETE {<value1>, <user_key>, ts1}, {<value2>, <user_key>, ts2}, …
FROM Users_By_Location WHERE KEY = <user_key>;

UPDATE Users_Index_Entries SET {"location", ts3} = <value3>
WHERE KEY = <user_key>;

UPDATE Indexes SET {<value3>, <user_key>, ts3) = null
WHERE KEY = "Users_By_Location";

UPDATE Users SET location = <value3>
WHERE KEY = <user_key>;

APPLY BATCH
Remove Old Column Values
SELECT {"location"}..{"location",*}
FROM Users_Index_Entries WHERE KEY = <user_key>;

BEGIN BATCH

DELETE {"location", ts1}, {"location", ts2}, …
FROM Users_Index_Entries WHERE KEY = <user_key>;

DELETE {<value1>, <user_key>, ts1}, {<value2>, <user_key>, ts2}, …
FROM Users_By_Location WHERE KEY = <user_key>;

UPDATE Users_Index_Entries SET {"location", ts3} = <value3>
WHERE KEY = <user_key>;

UPDATE Indexes SET {<value3>, <user_key>, ts3) = null
WHERE KEY = "Users_By_Location";

UPDATE Users SET location = <value3>
WHERE KEY = <user_key>;

APPLY BATCH
Insert New Column Values In Index
SELECT {"location"}..{"location",*}
FROM Users_Index_Entries WHERE KEY = <user_key>;

BEGIN BATCH

DELETE {"location", ts1}, {"location", ts2}, …
FROM Users_Index_Entries WHERE KEY = <user_key>;

DELETE {<value1>, <user_key>, ts1}, {<value2>, <user_key>, ts2}, …
FROM Users_By_Location WHERE KEY = <user_key>;

UPDATE Users_Index_Entries SET {"location", ts3} = <value3>
WHERE KEY = <user_key>;

UPDATE Indexes SET {<value3>, <user_key>, ts3) = null
WHERE KEY = "Users_By_Location";

UPDATE Users SET location = <value3>
WHERE KEY = <user_key>;

APPLY BATCH
Set New Value For User
SELECT {"location"}..{"location",*}
FROM Users_Index_Entries WHERE KEY = <user_key>;

BEGIN BATCH

DELETE {"location", ts1}, {"location", ts2}, …
FROM Users_Index_Entries WHERE KEY = <user_key>;

DELETE {<value1>, <user_key>, ts1}, {<value2>, <user_key>, ts2}, …
FROM Users_By_Location WHERE KEY = <user_key>;

UPDATE Users_Index_Entries SET {"location", ts3} = <value3>
WHERE KEY = <user_key>;

UPDATE Indexes SET {<value3>, <user_key>, ts3) = null
WHERE KEY = "Users_By_Location";

UPDATE Users SET location = <value3>
WHERE KEY = <user_key>;

APPLY BATCH
Frequently Asked Questions
•  Do I need locking for concurrency?
   –  No, the index will always be eventually consistent
•  What if something goes wrong?
   –  You will have to have provisions to repeat the batch operation
      until it completes, but its idempotent, so it’s ok
•  Can’t I get a false positive?
   –  Depending on updates being in-flight, you might get a false
      positive, if this is a problem, filter on read
•  Who else is using this approach?
   –  Actually very common with lots of variations, this isn’t the only
      way to do this but at least composite format is now standard
Some Things To Think About
•  Indexes can be derived from column values
   –  Create a “last_name, first_name” index from a
      “fullname” column
   –  Unroll a JSON object to construct deep indexes of
      serialized JSON structures

•  Include additional denormalized values in the
   index for faster lookups
•  Use composites for column values too not just
   column names
How can I learn more?
Sample implementation using Hector:
https://github.com/edanuff/CassandraIndexedCollections

JPA implementation using this for Hector:
https://github.com/riptano/hector-jpa

Jira entry on native composites for Cassandra:
https://issues.apache.org/jira/browse/CASSANDRA-2231

Background blog posts:
http://www.anuff.com/2011/02/indexing-in-cassandra.html
http://www.anuff.com/2010/07/secondary-indexes-in-cassandra.html
What is Usergrid?
•  Cloud PaaS backend for mobile and rich-client applications
•  Powered by Cassandra

•  In private Beta now
•  Entire stack to be open-sourced in August so people can
   run their own
•  Sign up to get admitted to Beta and to be notified when
   about source code
                  http://www.usergrid.com
Thank You

More Related Content

What's hot

Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, Scala
Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, ScalaLambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, Scala
Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, ScalaHelena Edelson
 
Enable GoldenGate Monitoring with OEM 12c/JAgent
Enable GoldenGate Monitoring with OEM 12c/JAgentEnable GoldenGate Monitoring with OEM 12c/JAgent
Enable GoldenGate Monitoring with OEM 12c/JAgentBobby Curtis
 
Introduction to Apache Flink - Fast and reliable big data processing
Introduction to Apache Flink - Fast and reliable big data processingIntroduction to Apache Flink - Fast and reliable big data processing
Introduction to Apache Flink - Fast and reliable big data processingTill Rohrmann
 
Deep Dive into the New Features of Apache Spark 3.1
Deep Dive into the New Features of Apache Spark 3.1Deep Dive into the New Features of Apache Spark 3.1
Deep Dive into the New Features of Apache Spark 3.1Databricks
 
Apache Calcite Tutorial - BOSS 21
Apache Calcite Tutorial - BOSS 21Apache Calcite Tutorial - BOSS 21
Apache Calcite Tutorial - BOSS 21Stamatis Zampetakis
 
Under the Hood of a Shard-per-Core Database Architecture
Under the Hood of a Shard-per-Core Database ArchitectureUnder the Hood of a Shard-per-Core Database Architecture
Under the Hood of a Shard-per-Core Database ArchitectureScyllaDB
 
Oracle 21c: New Features and Enhancements of Data Pump & TTS
Oracle 21c: New Features and Enhancements of Data Pump & TTSOracle 21c: New Features and Enhancements of Data Pump & TTS
Oracle 21c: New Features and Enhancements of Data Pump & TTSChristian Gohmann
 
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...Amazon Web Services
 
Introduction to Cassandra Architecture
Introduction to Cassandra ArchitectureIntroduction to Cassandra Architecture
Introduction to Cassandra Architecturenickmbailey
 
Introduction to Cassandra Basics
Introduction to Cassandra BasicsIntroduction to Cassandra Basics
Introduction to Cassandra Basicsnickmbailey
 
3D: DBT using Databricks and Delta
3D: DBT using Databricks and Delta3D: DBT using Databricks and Delta
3D: DBT using Databricks and DeltaDatabricks
 
Building Robust ETL Pipelines with Apache Spark
Building Robust ETL Pipelines with Apache SparkBuilding Robust ETL Pipelines with Apache Spark
Building Robust ETL Pipelines with Apache SparkDatabricks
 
Apache Calcite (a tutorial given at BOSS '21)
Apache Calcite (a tutorial given at BOSS '21)Apache Calcite (a tutorial given at BOSS '21)
Apache Calcite (a tutorial given at BOSS '21)Julian Hyde
 

What's hot (20)

Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, Scala
Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, ScalaLambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, Scala
Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, Scala
 
Graph Databases
Graph DatabasesGraph Databases
Graph Databases
 
Enable GoldenGate Monitoring with OEM 12c/JAgent
Enable GoldenGate Monitoring with OEM 12c/JAgentEnable GoldenGate Monitoring with OEM 12c/JAgent
Enable GoldenGate Monitoring with OEM 12c/JAgent
 
Apache HBase™
Apache HBase™Apache HBase™
Apache HBase™
 
Introduction to Apache Flink - Fast and reliable big data processing
Introduction to Apache Flink - Fast and reliable big data processingIntroduction to Apache Flink - Fast and reliable big data processing
Introduction to Apache Flink - Fast and reliable big data processing
 
Apache Spark Overview
Apache Spark OverviewApache Spark Overview
Apache Spark Overview
 
TiDB Introduction
TiDB IntroductionTiDB Introduction
TiDB Introduction
 
Deep Dive into the New Features of Apache Spark 3.1
Deep Dive into the New Features of Apache Spark 3.1Deep Dive into the New Features of Apache Spark 3.1
Deep Dive into the New Features of Apache Spark 3.1
 
Apache Calcite Tutorial - BOSS 21
Apache Calcite Tutorial - BOSS 21Apache Calcite Tutorial - BOSS 21
Apache Calcite Tutorial - BOSS 21
 
Under the Hood of a Shard-per-Core Database Architecture
Under the Hood of a Shard-per-Core Database ArchitectureUnder the Hood of a Shard-per-Core Database Architecture
Under the Hood of a Shard-per-Core Database Architecture
 
Oracle 21c: New Features and Enhancements of Data Pump & TTS
Oracle 21c: New Features and Enhancements of Data Pump & TTSOracle 21c: New Features and Enhancements of Data Pump & TTS
Oracle 21c: New Features and Enhancements of Data Pump & TTS
 
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
 
Introduction to Cassandra Architecture
Introduction to Cassandra ArchitectureIntroduction to Cassandra Architecture
Introduction to Cassandra Architecture
 
Cassandra Database
Cassandra DatabaseCassandra Database
Cassandra Database
 
Introduction to Cassandra Basics
Introduction to Cassandra BasicsIntroduction to Cassandra Basics
Introduction to Cassandra Basics
 
3D: DBT using Databricks and Delta
3D: DBT using Databricks and Delta3D: DBT using Databricks and Delta
3D: DBT using Databricks and Delta
 
Building Robust ETL Pipelines with Apache Spark
Building Robust ETL Pipelines with Apache SparkBuilding Robust ETL Pipelines with Apache Spark
Building Robust ETL Pipelines with Apache Spark
 
Apache Calcite (a tutorial given at BOSS '21)
Apache Calcite (a tutorial given at BOSS '21)Apache Calcite (a tutorial given at BOSS '21)
Apache Calcite (a tutorial given at BOSS '21)
 
Introduction to Apache Spark
Introduction to Apache SparkIntroduction to Apache Spark
Introduction to Apache Spark
 
Introduction to NoSQL
Introduction to NoSQLIntroduction to NoSQL
Introduction to NoSQL
 

Similar to Indexing in Cassandra

Building a Mobile Data Platform with Cassandra - Apigee Under the Hood (Webcast)
Building a Mobile Data Platform with Cassandra - Apigee Under the Hood (Webcast)Building a Mobile Data Platform with Cassandra - Apigee Under the Hood (Webcast)
Building a Mobile Data Platform with Cassandra - Apigee Under the Hood (Webcast)Apigee | Google Cloud
 
ElasticSearch for .NET Developers
ElasticSearch for .NET DevelopersElasticSearch for .NET Developers
ElasticSearch for .NET DevelopersBen van Mol
 
MongoDB.local DC 2018: Tips and Tricks for Avoiding Common Query Pitfalls
MongoDB.local DC 2018: Tips and Tricks for Avoiding Common Query PitfallsMongoDB.local DC 2018: Tips and Tricks for Avoiding Common Query Pitfalls
MongoDB.local DC 2018: Tips and Tricks for Avoiding Common Query PitfallsMongoDB
 
NOSQL and Cassandra
NOSQL and CassandraNOSQL and Cassandra
NOSQL and Cassandrarantav
 
Learn c++ Programming Language
Learn c++ Programming LanguageLearn c++ Programming Language
Learn c++ Programming LanguageSteve Johnson
 
Redis Indices (#RedisTLV)
Redis Indices (#RedisTLV)Redis Indices (#RedisTLV)
Redis Indices (#RedisTLV)Itamar Haber
 
Milot Shala - C++ (OSCAL2014)
Milot Shala - C++ (OSCAL2014)Milot Shala - C++ (OSCAL2014)
Milot Shala - C++ (OSCAL2014)Open Labs Albania
 
Intro to Elasticsearch
Intro to ElasticsearchIntro to Elasticsearch
Intro to ElasticsearchClifford James
 
Learn c sharp at amc square learning
Learn c sharp at amc square learningLearn c sharp at amc square learning
Learn c sharp at amc square learningASIT Education
 
Rails Tips and Best Practices
Rails Tips and Best PracticesRails Tips and Best Practices
Rails Tips and Best PracticesDavid Keener
 
How ElasticSearch lives in my DevOps life
How ElasticSearch lives in my DevOps lifeHow ElasticSearch lives in my DevOps life
How ElasticSearch lives in my DevOps life琛琳 饶
 
Kotlin coroutines and spring framework
Kotlin coroutines and spring frameworkKotlin coroutines and spring framework
Kotlin coroutines and spring frameworkSunghyouk Bae
 
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017StampedeCon
 

Similar to Indexing in Cassandra (20)

Building a Mobile Data Platform with Cassandra - Apigee Under the Hood (Webcast)
Building a Mobile Data Platform with Cassandra - Apigee Under the Hood (Webcast)Building a Mobile Data Platform with Cassandra - Apigee Under the Hood (Webcast)
Building a Mobile Data Platform with Cassandra - Apigee Under the Hood (Webcast)
 
Elastic tire demo
Elastic tire demoElastic tire demo
Elastic tire demo
 
ElasticSearch for .NET Developers
ElasticSearch for .NET DevelopersElasticSearch for .NET Developers
ElasticSearch for .NET Developers
 
MongoDB.local DC 2018: Tips and Tricks for Avoiding Common Query Pitfalls
MongoDB.local DC 2018: Tips and Tricks for Avoiding Common Query PitfallsMongoDB.local DC 2018: Tips and Tricks for Avoiding Common Query Pitfalls
MongoDB.local DC 2018: Tips and Tricks for Avoiding Common Query Pitfalls
 
NOSQL and Cassandra
NOSQL and CassandraNOSQL and Cassandra
NOSQL and Cassandra
 
Introduction to c#
Introduction to c#Introduction to c#
Introduction to c#
 
Learn c++ Programming Language
Learn c++ Programming LanguageLearn c++ Programming Language
Learn c++ Programming Language
 
Redis Indices (#RedisTLV)
Redis Indices (#RedisTLV)Redis Indices (#RedisTLV)
Redis Indices (#RedisTLV)
 
Milot Shala - C++ (OSCAL2014)
Milot Shala - C++ (OSCAL2014)Milot Shala - C++ (OSCAL2014)
Milot Shala - C++ (OSCAL2014)
 
Java Tutorial
Java Tutorial Java Tutorial
Java Tutorial
 
c++ Unit III - PPT.pptx
c++ Unit III - PPT.pptxc++ Unit III - PPT.pptx
c++ Unit III - PPT.pptx
 
Intro to Elasticsearch
Intro to ElasticsearchIntro to Elasticsearch
Intro to Elasticsearch
 
Learn c sharp at amc square learning
Learn c sharp at amc square learningLearn c sharp at amc square learning
Learn c sharp at amc square learning
 
Rails Tips and Best Practices
Rails Tips and Best PracticesRails Tips and Best Practices
Rails Tips and Best Practices
 
How ElasticSearch lives in my DevOps life
How ElasticSearch lives in my DevOps lifeHow ElasticSearch lives in my DevOps life
How ElasticSearch lives in my DevOps life
 
Intro_2.ppt
Intro_2.pptIntro_2.ppt
Intro_2.ppt
 
Intro.ppt
Intro.pptIntro.ppt
Intro.ppt
 
Intro.ppt
Intro.pptIntro.ppt
Intro.ppt
 
Kotlin coroutines and spring framework
Kotlin coroutines and spring frameworkKotlin coroutines and spring framework
Kotlin coroutines and spring framework
 
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
 

Recently uploaded

08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking MenDelhi Call girls
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...Martijn de Jong
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityPrincipled Technologies
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Neo4j
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)wesley chun
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Scriptwesley chun
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationRadu Cotescu
 
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessAdvantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessPixlogix Infotech
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CVKhem
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxKatpro Technologies
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 

Recently uploaded (20)

08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessAdvantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your Business
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 

Indexing in Cassandra

  • 1. Cassandra Indexing Techniques Ed Anuff Founder, Usergrid Cassandra Summit SF July, 2011
  • 2. Agenda •  Background •  Basics of Indexes •  Native Secondary Indexes •  "Wide rows" and CF-based Indexes •  Inverted-indexes Using SuperColumns •  Inverted-indexes Using Composite Columns •  Q&A
  • 3. Background This presentation is based on: •  What we learned over the last year in building a highly indexed system on Cassandra •  Participation in the Hector client project •  Common questions and issues posted to the Cassandra mailing lists
  • 4. Brief History - Cassandra 0.6 •  No built-in secondary indexes •  All indexes were custom-built, usually using super-columns •  Pros –  Forced you to learn how Cassandra works •  Cons –  Lots of work –  Super-columns proved a dead-end
  • 5. Brief History - Cassandra 0.7 •  Built-in secondary indexes •  New users flocked to these •  Pros –  Easy to use, out of the box •  Cons –  Deceptively similar to SQL indexes but not the same –  Reinforce data modeling that plays against Cassandra’s strengths
  • 6. Present Day •  New users can now get started with Cassandra without really understanding it (CQL, etc.) •  Veteran users are using advanced techniques (Composites, etc.) that aren’t really documented anywhere* •  New user panic mode when they try to go to the next level and suddenly find themselves in the deep end *Actually, they are, Google is your friend…
  • 7. Let’s try to bridge the gap…
  • 8. A Quick Review There are essentially two ways of finding rows: The Primary Index (row keys) Alternate Indexes (everything else)
  • 9. The “Primary Index” •  The “primary index” is your row key* •  Sometimes it’s meaningful (“natural id”): Users = { "edanuff" : { email: "ed@anuff.com" } } *Yeah. No. But if it helps, yes.
  • 10. The “Primary Index” •  The “primary index” is your row key* •  But usually it’s not: Users = { "4e3c0423-aa84-11e0-a743-58b0356a4c0a" : { username: "edanuff", email: "ed@anuff.com" } } *Yeah. No. But if it helps, yes.
  • 11. Get vs. Find •  Using the row key is the best way to retrieve something if you’ve got a precise and immutable 1:1 mapping •  If you find yourself ever planning to iterate over keys, you’re probably doing something wrong –  i.e. avoid the Order Preserving Partitioner* •  Use alternate indexes to find (search) things *Feel free to disregard, but don’t complain later
  • 12. Alternate Indexes Anything other than using the row key: •  Native secondary indexes •  Wide rows as lookup and grouping tables •  Custom secondary indexes Remember, there is no magic here…
  • 13. Native Secondary Indexes •  Easy to use •  Look (deceptively) like SQL indexes, especially when used with CQL CREATE INDEX ON Users(username); SELECT * FROM Users WHERE username="edanuff";
  • 14. Under The Hood •  Every index is stored as its own "hidden" CF •  Nodes index the rows they store •  When you issue a query, it gets sent to all nodes •  Currently does equality operations, the range operations get performed by in memory by coordinator node
  • 15. Some Limitations •  Not recommended for high cardinality values (i.e. timestamps, birthdates, keywords, etc.) •  Requires at least one equality comparison in a query – not great for less-than/greater-than/range queries •  Unsorted - results are in token order, not query value order •  Limited to search on data types Cassandra natively understands
  • 16. I can’t live with those limitations, what are my options? Complain on the mailing list Switch to Mongo Build my indexes in my application
  • 17. That sounds hard… Actually, it’s not, but it does require us to revisit some of Cassandra’s unique features
  • 18. Wide Rows “Why would a row need 2B columns”? •  Basis of all indexing, organizing, and relationships in Cassandra •  If your data model has no rows with over a hundred columns, you’re either doing something wrong or shouldn’t be using Cassandra* *IMHO J
  • 19. Conventional Rows As Records Users = { "4e3c0423-…” : { username: "edanuff", email: "ed@anuff.com", … : … }, "e5d61f2b-…” : { username: "jdoe", email: "john.doe@gmail.com", … : … } }
  • 20. Wide Row For Grouping Departments = { "Engineering" : { "e5d61f2b-…" : null, "e5d61f2b-…" : null, Column names "e66afd40-…" : null, are keys of rows "e719962b-…" : null, in “Users” CF "e78ece0f-…" : null, "e80a17ba-…" : null, … : …, } }
  • 21. Wide Row As A Simple Index Comparator = “UTF8Type” Indexes = { "User_Keys_By_Last_Name" : { "adams" : "e5d61f2b-…", "alden" : "e80a17ba-…", "anderson" : "e5d61f2b-…", "davis" : "e719962b-…", "doe" : "e78ece0f-…", "franks" : "e66afd40-…", … : …, } }
  • 22. Column Families As Indexes •  CF column operations very fast •  Column slices can be retrieved by range, are always sorted, can be reversed, etc. •  If target key a TimeUUID you get both grouping and sort by timestamp –  Good for inboxes, feeds, logs, etc. (Twissandra) •  Best option when you need to combine groups, sort, and search –  Search friends list, inbox, etc.
  • 23. But, only works for 1:1 What happens when I’ve got one to many? Indexes = { "User_Keys_By_Last_Name" : { "adams" : "e5d61f2b-…", "alden" : "e80a17ba-…", "anderson" : "e5d61f2b-…", ✖! "anderson" : "e719962b-…", Not Allowed "doe" : "e78ece0f-…", "franks" : "e66afd40-…", … : …, } }
  • 24. SuperColumns to the rescue? Indexes = { "User_Keys_By_Last_Name" : { "adams" : { "e5d61f2b-…" : null }, "anderson" : { "e5d61f2b-…" : null, "e66afd40-…" : null } } }
  • 25. Use with caution •  Not officially deprecated, but not highly recommended either •  Sorts only on the supercolumn, not subcolumn •  Some performance issues •  What if I want more nesting? –  Can subcolumns have subcolumns? NO! •  Anecdotally, many projects have moved away from using supercolumns
  • 26. So, let’s revisit regular CF’s What happens when I’ve got one to many? Indexes = { "User_Keys_By_Last_Name" : { "adams" : "e5d61f2b-…", "alden" : "e80a17ba-…", "anderson" : "e5d61f2b-…", ✖! "anderson" : "e719962b-…", Not Allowed "doe" : "e78ece0f-…", "franks" : "e66afd40-…", … : …, } }
  • 27. So, let’s revisit regular CF’s What if we could turn it back to one to one? Indexes = { "User_Keys_By_Last_Name" : { {"adams", 1} : "e5d…", {"alden", 1} : "e80…", {"anderson", 1} : "e5f…", ✔! {"anderson", 2} : "e71…", Allowed!!! {"doe", 1} : "e78…", {"franks", 1} : "e66…", … : …, } }
  • 28. Composite Column Names Comparator = “CompositeType” or “DynamicCompositeType” {"anderson", 1, 1, 1, …} : "e5f…" 1..N Components Build your column name out of one or more “component” values which can be of any of the columns types Cassandra supports (i.e. UTF8, IntegerType, UUIDType, etc.)
  • 29. Composite Column Names Comparator = “CompositeType” or “DynamicCompositeType” {"anderson", 1, 1, 1, …} : "e5f…" {"anderson", 1, 1, 2, …} : "e5f…" {"anderson", 1, 2, 1, …} : "e5f…" {"anderson", 1, 2, 2, …} : "e5f…" Sorts by component values using each component type’s sort order Retrieve using normal column slice technique
  • 30. Two Types Of Composites - column_families: - name: My_Composite_Index_CF - compare_with: CompositeType(UTF8Type, UUIDType) - name: My_Dynamic_Composite_Index_CF - compare_with: DynamicCompositeType(s=>UTF8Type, u=>UUIDType) Main difference for use in indexes is whether you need to create one CF per index vs one CF for all indexes with one row per index
  • 31. Static Composites - column_families: - name: My_Composite_Index_CF - compare_with: CompositeType(UTF8Type, UUIDType) {"anuff", "e5f…"} : "e5f…" Fixed # and order defined in column configuration
  • 32. Dynamic Composites - column_families: - name: My_Dynamic_Composite_Index_CF - compare_with: DynamicCompositeType(s=>UTF8Type, u=>UUIDType) {"anuff", "e5f…", "e5f…"} : "…" Any # and order of types at runtime
  • 33. Typical Composite Index Entry {<term 1>, …, <term N>, <key>, <ts>} <term 1…N> - terms to query on (i.e. last_name, first_name) <key> - target row key <ts> - unique timestamp, usually time-based UUID
  • 34. How does this work? •  Queries are easy –  regular column slice operations •  Updates are harder –  Need to remove old value and insert the new value –  Uh oh, read before write??!!!
  • 35. Example – Users By Location •  We need 3 Column Families (not 2) •  First 2 CF’s are obvious: –  Users –  Indexes •  We also need a third CF: –  Users_Index_Entries
  • 36. Users CF Comparator = “BytesType” Users = { <user_key> : { "username" : "…", "location" : <location>, … : …, } }
  • 37. Indexes CF Comparator = “CompositeType” Indexes = { "Users_By_Location" : { {<location>, <user_key>, <ts>} : …, … : …, } }
  • 38. Users Index Entries CF Comparator = “CompositeType” Users_Index_Entries = { <user_key> : { {"location", <ts 1>} : <location 1>, {"location", <ts 2>} : <location 2>, {"location", <ts N>} : <location N>, {"last_name", <ts 1>} : "…", … : …, } }
  • 39. Users Index Entries CF Comparator = “CompositeType” Users_Index_Entries = { <user_key> : { {"location", <ts 1>} : <location 1>, {"location", <ts 2>} : <location 2>, {"location", <ts N>} : <location N>, {"last_name", <ts 1>} : "…", … : …, } }
  • 40. Users Index Entries CF Comparator = “CompositeType” Users_Index_Entries = { <user_key> : { {"location", <ts 1>} : <location 1>, {"location", <ts 2>} : <location 2>, {"location", <ts N>} : <location N>, {"last_name", <ts 1>} : "…", … : …, } }
  • 41. Updating The Index •  We read previous index values from the Users_Index_Entries CF rather than the Users CF to deal with concurrency •  Columns in Index CF and Users_Index_Entries CF are timestamped so no locking is needed for concurrent updates
  • 42. Get Old Values For Column SELECT {"location"}..{"location",*} FROM Users_Index_Entries WHERE KEY = <user_key>; BEGIN BATCH DELETE {"location", ts1}, {"location", ts2}, … FROM Users_Index_Entries WHERE KEY = <user_key>; DELETE {<value1>, <user_key>, ts1}, {<value2>, <user_key>, ts2}, … FROM Users_By_Location WHERE KEY = <user_key>; UPDATE Users_Index_Entries SET {"location", ts3} = <value3> WHERE KEY = <user_key>; UPDATE Indexes SET {<value3>, <user_key>, ts3) = null WHERE KEY = "Users_By_Location"; UPDATE Users SET location = <value3> WHERE KEY = <user_key>; APPLY BATCH
  • 43. Remove Old Column Values SELECT {"location"}..{"location",*} FROM Users_Index_Entries WHERE KEY = <user_key>; BEGIN BATCH DELETE {"location", ts1}, {"location", ts2}, … FROM Users_Index_Entries WHERE KEY = <user_key>; DELETE {<value1>, <user_key>, ts1}, {<value2>, <user_key>, ts2}, … FROM Users_By_Location WHERE KEY = <user_key>; UPDATE Users_Index_Entries SET {"location", ts3} = <value3> WHERE KEY = <user_key>; UPDATE Indexes SET {<value3>, <user_key>, ts3) = null WHERE KEY = "Users_By_Location"; UPDATE Users SET location = <value3> WHERE KEY = <user_key>; APPLY BATCH
  • 44. Insert New Column Values In Index SELECT {"location"}..{"location",*} FROM Users_Index_Entries WHERE KEY = <user_key>; BEGIN BATCH DELETE {"location", ts1}, {"location", ts2}, … FROM Users_Index_Entries WHERE KEY = <user_key>; DELETE {<value1>, <user_key>, ts1}, {<value2>, <user_key>, ts2}, … FROM Users_By_Location WHERE KEY = <user_key>; UPDATE Users_Index_Entries SET {"location", ts3} = <value3> WHERE KEY = <user_key>; UPDATE Indexes SET {<value3>, <user_key>, ts3) = null WHERE KEY = "Users_By_Location"; UPDATE Users SET location = <value3> WHERE KEY = <user_key>; APPLY BATCH
  • 45. Set New Value For User SELECT {"location"}..{"location",*} FROM Users_Index_Entries WHERE KEY = <user_key>; BEGIN BATCH DELETE {"location", ts1}, {"location", ts2}, … FROM Users_Index_Entries WHERE KEY = <user_key>; DELETE {<value1>, <user_key>, ts1}, {<value2>, <user_key>, ts2}, … FROM Users_By_Location WHERE KEY = <user_key>; UPDATE Users_Index_Entries SET {"location", ts3} = <value3> WHERE KEY = <user_key>; UPDATE Indexes SET {<value3>, <user_key>, ts3) = null WHERE KEY = "Users_By_Location"; UPDATE Users SET location = <value3> WHERE KEY = <user_key>; APPLY BATCH
  • 46. Frequently Asked Questions •  Do I need locking for concurrency? –  No, the index will always be eventually consistent •  What if something goes wrong? –  You will have to have provisions to repeat the batch operation until it completes, but its idempotent, so it’s ok •  Can’t I get a false positive? –  Depending on updates being in-flight, you might get a false positive, if this is a problem, filter on read •  Who else is using this approach? –  Actually very common with lots of variations, this isn’t the only way to do this but at least composite format is now standard
  • 47. Some Things To Think About •  Indexes can be derived from column values –  Create a “last_name, first_name” index from a “fullname” column –  Unroll a JSON object to construct deep indexes of serialized JSON structures •  Include additional denormalized values in the index for faster lookups •  Use composites for column values too not just column names
  • 48. How can I learn more? Sample implementation using Hector: https://github.com/edanuff/CassandraIndexedCollections JPA implementation using this for Hector: https://github.com/riptano/hector-jpa Jira entry on native composites for Cassandra: https://issues.apache.org/jira/browse/CASSANDRA-2231 Background blog posts: http://www.anuff.com/2011/02/indexing-in-cassandra.html http://www.anuff.com/2010/07/secondary-indexes-in-cassandra.html
  • 49. What is Usergrid? •  Cloud PaaS backend for mobile and rich-client applications •  Powered by Cassandra •  In private Beta now •  Entire stack to be open-sourced in August so people can run their own •  Sign up to get admitted to Beta and to be notified when about source code http://www.usergrid.com