A Deep Dive into Apache Cassandra for .NET Developers

A Deep Dive into Apache Cassandra for
.NET Developers
Luke Tillman (@LukeTillman)
Language Evangelist at DataStax

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

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

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

What is Cassandra and how does it work?
5

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

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

What is Cassandra?
• Fault Tolerant
– Nodes Down != Database Down
– Datacenter Down != Database Down
8

What is Cassandra?
• Fully Replicated
• Clients write local
• Data syncs across WAN
• Replication Factor per DC
9
US Europe
Client

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

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

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

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

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)

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)

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

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

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)
);

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
Murmur3
id: ltillman Murmur3: A
B
AD
C
AB
A
CD
D
BC
RF=3

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

Writes on a single node
• Client makes a write request
Client
UPDATE users
SET firstname = 'Luke'
WHERE id = 'ltillman'
Disk
Memory

Client
UPDATE users
Commit Log
id='ltillman', firstname='Luke'
…
…
Disk
Memory
• Data is appended to the Commit Log
• Append only, sequential IO == FAST

Client
UPDATE users
Commit Log
…
…
Disk
Memory
Memtable for Users Some
Other
Memtableid='ltillman' firstname='Luke' lastname='Tillman'
• Data is written/merged into Memtable

Client
UPDATE users
Commit Log
…
…
Disk
Memory
Other
• Server acknowledges to client
• Writes in C* are FAST due to simplicity, log structured storage

Client
UPDATE users
Data Directory
Disk
Memory
Other
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)

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'
(timestamp=Newer)

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

Reads on a single node
• Client makes a read request
28
Client
SELECT firstname, lastname
FROM users
Disk
Memory

• Data is read from (possibly multiple) SSTables and merged
29
Client
FROM users
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'

• Any unflushed Memtable data is also merged
30
Client
FROM users
Disk
Memory
Memtable
for Users

• Client gets acknowledgement with the data
• Reads in Cassandra are also FAST but often limited by Disk IO
31
Client
FROM users
Disk
Memory

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'
(timestamp=Newer)

Cassandra Query Language (CQL)
33

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

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)
);

Schema Definition (DDL)
• One row per partition (familiar)
videoid uuid,
userid uuid,
name text,
description text,
tags set<text>,
);
name ...
Keyboard Cat ...
Nyan Cat ...
Original Grumpy Cat ...
videoid
689d56e5- …
93357d73- …
d978b136- …

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);

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);

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...';

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...';

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;

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

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

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

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

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

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)
);
userid uuid,
firstname text,
lastname text,
email text,
created_date timestamp,
PRIMARY KEY (userid)
);

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
videoid uuid,
userid uuid,
name text,
description text,
location text,
location_type int,
preview_image_location text,
tags set<text>,
);

Modeling Relationships – Client Side Joins
50
videoid uuid,
userid uuid,
name text,
description text,
location text,
location_type int,
preview_image_location text,
tags set<text>,
);
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

• What is the cost? Might be OK in small situations
• Do NOT scale
• Avoid when possible
51

52
videoid uuid,
userid uuid,
name text,
description text,
...
user_firstname text,
user_lastname text,
user_email text,
);
CREATE TABLE users_by_video (
videoid uuid,
userid uuid,
firstname text,
lastname text,
email text,
);
or

.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

.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

.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

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

.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 = ?");

.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);

.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);

.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

.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();

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

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]");

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

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)
{
}

Typical Pager UI in a Web Application
• Show page of records in UI and allow user to navigate

Typical Pager UI in a Web Application
• Automatic Paging – this is not the feature you are looking for

.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

.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

Installing Cassandra
• Planet Cassandra for all things C* (planetcassandra.org)

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)

In the Box – Command Line Tools
• Use cqlsh REPL for running CQL against your cluster
74

In the Box – Command Line Tools
• Use nodetool for information on your cluster (and lots more)
• On Windows, available under apache-cassandrabin folder
75

DevCenter
• Get it on DataStax
web site
(www.datastax.com)
• GUI for Cassandra
development (think
SQL Server
Management Studio
for Cassandra)
76

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

Questions? Overtime (use cases)?
Follow me for updates or to ask questions later: @LukeTillman
78

Overtime: Who’s using it?
79

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/

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

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/

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/

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/

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

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

Go forth and build awesome things!
Follow me for updates or to ask questions later: @LukeTillman
88

A Deep Dive into Apache Cassandra for .NET Developers

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