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A Deep Dive into Apache Cassandra for .NET Developers

.NET developers have a lot of options when it comes to databases these days. Apache Cassandra is a scalable, fault-tolerant database that has already found its way into more than 25% of the Fortune 100 and continues to grow in popularity. But what makes it different from the myriad of other options available? In this talk, we’ll take a deep dive into Cassandra and learn about:

- Cassandra’s internals and how it works

- CQL (the SQL-like query language for Cassandra)

- Data Modeling like a pro

- Tools available for developers

- Writing .NET code that talks to Cassandra

If there’s time and interest, we’ll finish up with how some companies are already using Cassandra to power services you probably interact with in your daily life. You’ll leave with all the tools you need to start build highly available .NET applications and services on top of Cassandra.

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A Deep Dive into Apache Cassandra for .NET Developers

  1. 1. A Deep Dive into Apache Cassandra for .NET Developers Luke Tillman (@LukeTillman) Language Evangelist at DataStax
  2. 2. Who are you?! • Evangelist with a focus on the .NET Community • Long-time .NET Developer • Recently presented at Cassandra Summit 2014 with Microsoft • Very Recent Denver Transplant 2
  3. 3. Why should I care? • Cassandra Core Principles – Ease of Use – Massive Scalability – High Performance – Always Available • Growth! 3 DB Engines Rankings (January 2014) http://db-engines.com/en/ranking
  4. 4. 1 What is Cassandra and how does it work? 2 Cassandra Query Language (CQL) 3 Data Modeling Like a Pro 4 .NET Driver for Cassandra 5 Tools and Code 4
  5. 5. What is Cassandra and how does it work? 5
  6. 6. What is Cassandra? • A Linearly Scaling and Fault Tolerant Distributed Database • Fully Distributed – Data spread over many nodes – All nodes participate in a cluster – All nodes are equal (masterless) – No SPOF (shared nothing) 6
  7. 7. What is Cassandra? • Linearly Scaling – Have More Data? Add more nodes. – Need More Throughput? Add more nodes. 7 http://techblog.netflix.com/2011/11/benchmarking-cassandra-scalability-on.html
  8. 8. What is Cassandra? • Fault Tolerant – Nodes Down != Database Down – Datacenter Down != Database Down 8
  9. 9. What is Cassandra? • Fully Replicated • Clients write local • Data syncs across WAN • Replication Factor per DC 9 US Europe Client
  10. 10. Cassandra and the CAP Theorem • The CAP Theorem limits what distributed systems can do • Consistency • Availability • Partition Tolerance • Limits? “Pick 2 out of 3” • Cassandra is an AP system that is Eventually Consistent 10
  11. 11. Two knobs control Cassandra fault tolerance • Replication Factor (server side) – How many copies of the data should exist? 11 Client B AD C AB A CD D BC Write A RF=3
  12. 12. Two knobs control Cassandra fault tolerance • Consistency Level (client side) – How many replicas do we need to hear from before we acknowledge? 12 Client B AD C AB A CD D BC Write A CL=QUORUM Client B AD C AB A CD D BC Write A CL=ONE
  13. 13. Consistency Levels • Applies to both Reads and Writes (i.e. is set on each query) • ONE – one replica from any DC • LOCAL_ONE – one replica from local DC • QUORUM – 51% of replicas from any DC • LOCAL_QUORUM – 51% of replicas from local DC • ALL – all replicas • TWO 13
  14. 14. Consistency Level and Speed • How many replicas we need to hear from can affect how quickly we can read and write data in Cassandra 14 Client B AD C AB A CD D BC 5 µs ack 300 µs ack 12 µs ack 12 µs ack Read A (CL=QUORUM)
  15. 15. Consistency Level and Availability • Consistency Level choice affects availability • For example, QUORUM can tolerate one replica being down and still be available (in RF=3) 15 Client B AD C AB A CD D BC A=2 A=2 A=2 Read A (CL=QUORUM)
  16. 16. Consistency Level and Eventual Consistency • Cassandra is an AP system that is Eventually Consistent so replicas may disagree • Column values are timestamped • In Cassandra, Last Write Wins (LWW) 16 Client B AD C AB A CD D BC A=2 Newer A=1 Older A=2 Read A (CL=QUORUM) Christos from Netflix: “Eventual Consistency != Hopeful Consistency” https://www.youtube.com/watch?v=lwIA8tsDXXE
  17. 17. Writes in the cluster • Fully distributed, no SPOF • Node that receives a request is the Coordinator for request • Any node can act as Coordinator 17 Client B AD C AB A CD D BC Write A (CL=ONE) Coordinator Node
  18. 18. Writes in the cluster – Data Distribution • Partition Key determines node placement 18 Partition Key id='pmcfadin' lastname='McFadin' id='jhaddad' firstname='Jon' lastname='Haddad' id='ltillman' firstname='Luke' lastname='Tillman' CREATE TABLE users ( id text, firstname text, lastname text, PRIMARY KEY (id) );
  19. 19. Writes in the cluster – Data Distribution • The Partition Key is hashed using a consistent hashing function (Murmur 3) and the output is used to place the data on a node • The data is also replicated to RF-1 other nodes 19 Partition Key id='ltillman' firstname='Luke' lastname='Tillman' Murmur3 id: ltillman Murmur3: A B AD C AB A CD D BC RF=3
  20. 20. Hashing – Back to Reality • Back in reality, Partition Keys actually hash to 128 bit numbers • Nodes in Cassandra own token ranges (i.e. hash ranges) 20 B AD C AB A CD D BC Range Start End A 0xC000000..1 0x0000000..0 B 0x0000000..1 0x4000000..0 C 0x4000000..1 0x8000000..0 D 0x8000000..1 0xC000000..0 Partition Key id='ltillman' Murmur3 0xadb95e99da887a8a4cb474db86eb5769
  21. 21. Writes on a single node • Client makes a write request Client UPDATE users SET firstname = 'Luke' WHERE id = 'ltillman' Disk Memory
  22. 22. Writes on a single node Client UPDATE users SET firstname = 'Luke' WHERE id = 'ltillman' Commit Log id='ltillman', firstname='Luke' … … Disk Memory • Data is appended to the Commit Log • Append only, sequential IO == FAST
  23. 23. Writes on a single node Client UPDATE users SET firstname = 'Luke' WHERE id = 'ltillman' Commit Log id='ltillman', firstname='Luke' … … Disk Memory Memtable for Users Some Other Memtableid='ltillman' firstname='Luke' lastname='Tillman' • Data is written/merged into Memtable
  24. 24. Writes on a single node Client UPDATE users SET firstname = 'Luke' WHERE id = 'ltillman' Commit Log id='ltillman', firstname='Luke' … … Disk Memory Memtable for Users Some Other Memtableid='ltillman' firstname='Luke' lastname='Tillman' • Server acknowledges to client • Writes in C* are FAST due to simplicity, log structured storage
  25. 25. Writes on a single node Client UPDATE users SET firstname = 'Luke' WHERE id = 'ltillman' Data Directory Disk Memory Memtable for Users Some Other Memtableid='ltillman' firstname='Luke' lastname='Tillman' Some Other SSTable SSTable #1 for Users SSTable #2 for Users • Once Memtable is full, data is flushed to disk as SSTable (Sorted String Table)
  26. 26. Compaction • Compactions merge and unify data in our SSTables • SSTables are immutable, so this is when we consolidate rows 26 SSTable #1 for Users SSTable #2 for Users SSTable #3 for Users id='ltillman' firstname='Lucas' (timestamp=Older) lastname='Tillman' id='ltillman' firstname='Luke' lastname='Tillman' id='ltillman' firstname='Luke' (timestamp=Newer)
  27. 27. Reads in the cluster • Same as writes in the cluster, reads are coordinated • Any node can be the Coordinator Node 27 Client B AD C AB A CD D BC Read A (CL=QUORUM) Coordinator Node
  28. 28. Reads on a single node • Client makes a read request 28 Client SELECT firstname, lastname FROM users WHERE id = 'ltillman' Disk Memory
  29. 29. Reads on a single node • Data is read from (possibly multiple) SSTables and merged 29 Client SELECT firstname, lastname FROM users WHERE id = 'ltillman' Disk Memory SSTable #1 for Users id='ltillman' firstname='Lucas' (timestamp=Older) lastname='Tillman' SSTable #2 for Users id='ltillman' firstname='Luke' (timestamp=Newer) firstname='Luke' lastname='Tillman'
  30. 30. Reads on a single node • Any unflushed Memtable data is also merged 30 Client SELECT firstname, lastname FROM users WHERE id = 'ltillman' Disk Memory firstname='Luke' lastname='Tillman' Memtable for Users
  31. 31. Reads on a single node • Client gets acknowledgement with the data • Reads in Cassandra are also FAST but often limited by Disk IO 31 Client SELECT firstname, lastname FROM users WHERE id = 'ltillman' Disk Memory firstname='Luke' lastname='Tillman'
  32. 32. Compaction - Revisited • Compactions merge and unify data in our SSTables, making them important to reads (less SSTables = less to read/merge) 32 SSTable #1 for Users SSTable #2 for Users SSTable #3 for Users id='ltillman' firstname='Lucas' (timestamp=Older) lastname='Tillman' id='ltillman' firstname='Luke' lastname='Tillman' id='ltillman' firstname='Luke' (timestamp=Newer)
  33. 33. Cassandra Query Language (CQL) 33
  34. 34. Data Structures • Keyspace is like RDBMS Database or Schema • Like RDBMS, Cassandra uses Tables to store data • Partitions can have one row (narrow) or multiple rows (wide) 34 Keyspace Tables Partitions Rows
  35. 35. Schema Definition (DDL) • Easy to define tables for storing data • First part of Primary Key is the Partition Key CREATE TABLE videos ( videoid uuid, userid uuid, name text, description text, tags set<text>, added_date timestamp, PRIMARY KEY (videoid) );
  36. 36. Schema Definition (DDL) • One row per partition (familiar) CREATE TABLE videos ( videoid uuid, userid uuid, name text, description text, tags set<text>, added_date timestamp, PRIMARY KEY (videoid) ); name ... Keyboard Cat ... Nyan Cat ... Original Grumpy Cat ... videoid 689d56e5- … 93357d73- … d978b136- …
  37. 37. Clustering Columns • Second part of Primary Key is Clustering Columns • Clustering columns affect ordering of data (on disk) • Multiple rows per partition 37 CREATE TABLE comments_by_video ( videoid uuid, commentid timeuuid, userid uuid, comment text, PRIMARY KEY (videoid, commentid) ) WITH CLUSTERING ORDER BY (commentid DESC);
  38. 38. Clustering Columns – Wide Rows (Partitions) • Use of Clustering Columns is where the (old) term “Wide Rows” comes from 38 videoid='0fe6a...' userid= 'ac346...' comment= 'Awesome!' commentid='82be1...' (10/1/2014 9:36AM) userid= 'f89d3...' comment= 'Garbage!' commentid='765ac...' (9/17/2014 7:55AM) CREATE TABLE comments_by_video ( videoid uuid, commentid timeuuid, userid uuid, comment text, PRIMARY KEY (videoid, commentid) ) WITH CLUSTERING ORDER BY (commentid DESC);
  39. 39. Inserts and Updates • Use INSERT or UPDATE to add and modify data • Both will overwrite data (no constraints like RDBMS) • INSERT and UPDATE functionally equivalent 39 INSERT INTO comments_by_video ( videoid, commentid, userid, comment) VALUES ( '0fe6a...', '82be1...', 'ac346...', 'Awesome!'); UPDATE comments_by_video SET userid = 'ac346...', comment = 'Awesome!' WHERE videoid = '0fe6a...' AND commentid = '82be1...';
  40. 40. TTL and Deletes • Can specify a Time to Live (TTL) in seconds when doing an INSERT or UPDATE • Use DELETE statement to remove data • Can optionally specify columns to remove part of a row 40 INSERT INTO comments_by_video ( ... ) VALUES ( ... ) USING TTL 86400; DELETE FROM comments_by_video WHERE videoid = '0fe6a...' AND commentid = '82be1...';
  41. 41. Querying • Use SELECT to get data from your tables • Always include Partition Key and optionally Clustering Columns • Can use ORDER BY and LIMIT • Use range queries (for example, by date) to slice partitions 41 SELECT * FROM comments_by_video WHERE videoid = 'a67cd...' LIMIT 10;
  42. 42. Data Modeling Like a Pro 42
  43. 43. Cassandra Data Modeling • Requires a different mindset than RDBMS modeling • Know your data and your queries up front • Queries drive a lot of the modeling decisions (i.e. “table per query” pattern) • Denormalize/Duplicate data at write time to do as few queries as possible come read time • Remember, disk is cheap and writes in Cassandra are FAST 43
  44. 44. Getting to Know Your Data 44 User id firstname lastname email password Video id name description location preview_image tags features Comment comment id adds timestamp posts timestamp 1 n n 1 1 n n m rates rating
  45. 45. Application Workflows 45 User Logs into site Show basic information about user Show videos added by a user Show comments posted by a user Search for a video by tag Show latest videos added to the site Show comments for a video Show ratings for a video Show video and its details
  46. 46. Queries come from Workflows 46 Users User Logs into site Find user by email address Show basic information about user Find user by id Comments Show comments for a video Find comments by video (latest first) Show comments posted by a user Find comments by user (latest first) Ratings Show ratings for a video Find ratings by video
  47. 47. Users – The Relational Way • Single Users table with all user data and an Id Primary Key • Add an index on email address to allow queries by email User Logs into site Find user by email address Show basic information about user Find user by id
  48. 48. Users – The Cassandra Way User Logs into site Find user by email address Show basic information about user Find user by id CREATE TABLE user_credentials ( email text, password text, userid uuid, PRIMARY KEY (email) ); CREATE TABLE users ( userid uuid, firstname text, lastname text, email text, created_date timestamp, PRIMARY KEY (userid) );
  49. 49. Modeling Relationships – Collection Types • Cassandra doesn’t support JOINs, but your data will still have relationships (and you can still model that in Cassandra) • One tool available is CQL collection types CREATE TABLE videos ( videoid uuid, userid uuid, name text, description text, location text, location_type int, preview_image_location text, tags set<text>, added_date timestamp, PRIMARY KEY (videoid) );
  50. 50. Modeling Relationships – Client Side Joins 50 CREATE TABLE videos ( videoid uuid, userid uuid, name text, description text, location text, location_type int, preview_image_location text, tags set<text>, added_date timestamp, PRIMARY KEY (videoid) ); CREATE TABLE users ( userid uuid, firstname text, lastname text, email text, created_date timestamp, PRIMARY KEY (userid) ); Currently requires query for video, followed by query for user by id based on results of first query
  51. 51. Modeling Relationships – Client Side Joins • What is the cost? Might be OK in small situations • Do NOT scale • Avoid when possible 51
  52. 52. Modeling Relationships – Client Side Joins 52 CREATE TABLE videos ( videoid uuid, userid uuid, name text, description text, ... user_firstname text, user_lastname text, user_email text, PRIMARY KEY (videoid) ); CREATE TABLE users_by_video ( videoid uuid, userid uuid, firstname text, lastname text, email text, PRIMARY KEY (videoid) ); or
  53. 53. .NET Driver for Cassandra 53
  54. 54. .NET and Cassandra • Open Source (on GitHub), available via NuGet • Bootstrap using the Builder and then reuse the ISession object Cluster cluster = Cluster.Builder() .AddContactPoint("127.0.0.1") .Build(); ISession session = cluster.Connect("killrvideo"); 54
  55. 55. .NET and Cassandra • Executing CQL with SimpleStatement • Sync and Async API available for executing statements • Use Async API for executing queries in parallel var videoId = Guid.NewGuid(); var statement = new SimpleStatement("SELECT * FROM videos WHERE videoid = ?", videoId); RowSet rows = await session.ExecuteAsync(statement); 55
  56. 56. .NET and Cassandra • Getting values from a RowSet is easy • Rowset is a collection of Row (IEnumerable<Row>) RowSet rows = await _session.ExecuteAsync(statement); foreach (Row row in rows) { var videoId = row.GetValue<Guid>("videoid"); var addedDate = row.GetValue<DateTimeOffset>("added_date"); var name = row.GetValue<string>("name"); } 56
  57. 57. CQL 3 Data Types to .NET Types • Full listing available in driver docs (http://www.datastax.com/docs) CQL 3 Data Type .NET Type bigint, counter long boolean bool decimal, float float double double int int uuid, timeuuid System.Guid text, varchar string (Encoding.UTF8) timestamp System.DateTimeOffset varint System.Numerics.BigInteger
  58. 58. .NET and Cassandra - PreparedStatement • SimpleStatement useful for one-off CQL execution (or when dynamic CQL is a possibility) • Use PreparedStatement for better performance • Pay the cost of Prepare once (server roundtrip) • Save the PreparedStatement instance and reuse PreparedStatement prepared = session.Prepare( "SELECT * FROM user_credentials WHERE email = ?");
  59. 59. .NET and Cassandra - PreparedStatement • Bind variable values to get BoundStatement for execution • Execution only has to send variable values • You will use these all the time • Remember: Prepare once, bind and execute many BoundStatement bound = prepared.Bind("luke.tillman@datastax.com"); RowSet rows = await _session.ExecuteAsync(bound);
  60. 60. .NET and Cassandra - BatchStatement • Add Simple/Bound statements to a batch BoundStatement bound = prepared.Bind(video.VideoId, video.Name); var simple = new SimpleStatement( "UPDATE videos SET name = ? WHERE videoid = ?" ).Bind(video.Name, video.VideoId); // Use an atomic batch to send over all the mutations var batchStatement = new BatchStatement(); batchStatement.Add(bound); batchStatement.Add(simple); RowSet rows = await _session.ExecuteAsync(batch);
  61. 61. .NET and Cassandra - BatchStatement • Batches are Logged (atomic) by default • Use when you want a group of mutations (statements) to all succeed or all fail (denormalizing at write time) • Really large batches are an anti-pattern (and Cassandra will warn you) • Not a performance optimization for bulk-loading data
  62. 62. .NET and Cassandra – Statement Options • Options like Consistency Level and Retry Policy are available at the Statement level • If not set on a statement, driver will fallback to defaults set when building/configuring the Cluster 62 IStatement bound = prepared.Bind("luke.tillman@datastax.com") .SetPageSize(100) .SetConsistencyLevel(ConsistencyLevel.LocalOne) .SetRetryPolicy(new DefaultRetryPolicy()) .EnableTracing();
  63. 63. Lightweight Transactions (LWT) • Use when you don’t want writes to step on each other • AKA Linearizable Consistency • Serial Isolation Level • Be sure to read the fine print: has a latency cost associated with using it, so use only where needed • The canonical example: unique user accounts
  64. 64. Lightweight Transactions (LWT) • Returns a column called [applied] indicating success/failure • Different from the relational world where you might expect an Exception (i.e. PrimaryKeyViolationException or similar) var statement = new SimpleStatement("INSERT INTO user_credentials (email, password) VALUES (?, ?) IF NOT EXISTS"); statement = statement.Bind("user1@killrvideo.com", "Password1!"); RowSet rows = await _session.ExecuteAsync(statement); var userInserted = rows.Single().GetValue<bool>("[applied]");
  65. 65. Automatic Paging • The Problem: Loading big result sets into memory is a recipe for disaster (OutOfMemoryExceptions, etc.) • Better to load and process a large result set in pages (chunks) • Doing this manually with Cassandra prior to 2.0 was a pain • Automatic Paging makes paging on a large RowSet transparent
  66. 66. Automatic Paging • Set a page size on a statement • Iterate over the resulting RowSet • As you iterate, new pages are fetched transparently when the Rows in the current page are exhausted • Will allow you to iterate until all pages are exhausted boundStatement = boundStatement.SetPageSize(100); RowSet rows = await _session.ExecuteAsync(boundStatement); foreach (Row row in rows) { }
  67. 67. Typical Pager UI in a Web Application • Show page of records in UI and allow user to navigate
  68. 68. Typical Pager UI in a Web Application • Automatic Paging – this is not the feature you are looking for
  69. 69. .NET and Cassandra • Mapping results to DTOs: if you like using CQL for querying, try Mapper component (formerly CqlPoco package) public class User { public Guid UserId { get; set; } public string Name { get; set; } } // Create a mapper from your session object var mapper = new Mapper(session); // Get a user by id from Cassandra or null if not found var user = client.SingleOrDefault<User>( "SELECT userid, name FROM users WHERE userid = ?", someUserId); 69
  70. 70. .NET and Cassandra • Mapping results to DTOs: if you like LINQ, use built-in LINQ provider [Table("users")] public class User { [Column("userid"), PartitionKey] public Guid UserId { get; set; } [Column("name")] public string Name { get; set; } } var user = session.GetTable<User>() .SingleOrDefault(u => u.UserId == someUserId) .Execute(); 70
  71. 71. Tools and Code 71
  72. 72. Installing Cassandra • Planet Cassandra for all things C* (planetcassandra.org)
  73. 73. Installing Cassandra • Windows installer is super easy way to do development and testing on your local machine • Production Cassandra deployments on Linux (Windows performance parity is coming in 3.0)
  74. 74. In the Box – Command Line Tools • Use cqlsh REPL for running CQL against your cluster 74
  75. 75. In the Box – Command Line Tools • Use nodetool for information on your cluster (and lots more) • On Windows, available under apache-cassandrabin folder 75
  76. 76. DevCenter • Get it on DataStax web site (www.datastax.com) • GUI for Cassandra development (think SQL Server Management Studio for Cassandra) 76
  77. 77. Sample Code – KillrVideo • Live demo available at http://www.killrvideo.com – Written in C# – Live Demo running in Azure – Open source: https://github.com/luketillman/killrvideo-csharp 77
  78. 78. Questions? Overtime (use cases)? Follow me for updates or to ask questions later: @LukeTillman 78
  79. 79. Overtime: Who’s using it? 79
  80. 80. Cassandra Adoption
  81. 81. Some Common Use Case Categories • Product Catalogs and Playlists • Internet of Things (IoT) and Sensor Data • Messaging (emails, IMs, alerts, comments) • Recommendation and Personalization • Fraud Detection • Time series and temporal ordered data http://planetcassandra.org/apache-cassandra-use-cases/
  82. 82. The “Slide Heard Round the World” • From Cassandra Summit 2014, got a lot of attention • 75,000+ nodes • 10s of PBs of data • Millions ops/s • One of the largest known Cassandra deployments 82
  83. 83. Spotify • Streaming music web service • > 24,000,000 music tracks • > 50TB of data in Cassandra Why Cassandra? • Was PostgreSQL, but hit scaling problems • Multi Datacenter Availability • Integration with Spark for data processing and analytics Usage • Catalog • User playlists • Artists following • Radio Stations • Event notifications 83 http://planetcassandra.org/blog/interview/spotify-scales-to-the-top-of-the-charts-with-apache-cassandra-at-40k-requestssecond/
  84. 84. eBay • Online auction site • > 250TB of data, dozens of nodes, multiple data centres • > 6 billion writes, > 5 billion reads per day Why Cassandra? • Low latency, high scale, multiple data centers • Suited for graph structures using wide rows Usage • Building next generation of recommendation engine • Storing user activity data • Updating models of user interests in real time 84 http://planetcassandra.org/blog/5-minute-c-interview-ebay/
  85. 85. FullContact • Contact management: from multiple sources, sync, de-dupe, APIs available • 2 clusters, dozens of nodes, running in AWS • Based here in Denver Why Cassandra? • Migated from MongoDB after running into scaling issues • Operational simplicity • Resilience and Availability Usage • Person API (search by email, Twitter handle, Facebook, or phone) • Searched data from multiple sources (ingested by Hadoop M/R jobs) • Resolved profiles 85 http://planetcassandra.org/blog/fullcontact-readies-their-search-platform-to-scale-moves-from-mongodb-to-apache-cassandra/
  86. 86. Instagram • Photo-sharing, video-sharing and social networking service • Originally AWS (Now Facebook data centers?) • > 20k writes/second, >15k reads/second Why Cassandra? • Migrated from Redis (problems keeping everything in memory) • No painful “sharding” process • 75% reduction in costs Usage • Auditing information – security, integrity, spam detection • News feed (“inboxes” or activity feed) – Likes, Follows, etc. 86 http://planetcassandra.org/blog/instagram-making-the-switch-to-cassandra-from-redis-75-instasavings/ Summit 2014 Presentation: https://www.youtube.com/watch?v=_gc94ITUitY
  87. 87. Netflix • TV and Movie streaming service • > 2700+ nodes on over 90 clusters • 4 Datacenters • > 1 Trillion operations per day Why Cassandra? • Migrated from Oracle • Massive amounts of data • Multi datacenter, No SPOF • No downtime for schema changes Usage • Everything! (Almost – 95% of DB use) • Example: Personalization – What titles do you play? – What do you play before/after? – Where did you pause? – What did you abandon watching after 5 minutes? 87 http://planetcassandra.org/blog/case-study-netflix/ Summit 2014 Presentation: https://www.youtube.com/watch?v=RMSNLP_ORg8&index=43&list=UUvP-AXuCr-naAeEccCfKwUA
  88. 88. Go forth and build awesome things! Follow me for updates or to ask questions later: @LukeTillman 88

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