Use Your MySQL Knowledge to Become an Instant Cassandra Guru

Percona Live Santa Clara 2014

Robert Hodges CEO

Continuent

Tim Callaghan VP/Engineering

Tokutek

Who are we? Robert Hodges ●  CEO at Continuent ●  Database nerd since 1982 starting with M204, RDBMS since

1990, NoSQL since 2012; designed Continuent Tungsten ●  Continuent offers clustering and replication for MySQL and

other fine DBMS types

Tim Callaghan ●  VP/Engineering at Tokutek ●  Long time database consumer (Oracle) and producer

(VoltDB, Tokutek) ●  Tokutek offers Fractal Tree indexes in MySQL (TokuDB) and

MongoDB (TokuMX)

Cassandra! Cassandra, used by NetFlix, eBay, Twitter, Reddit and

many others, is one of today's most popular NoSQL-databases in use. According to the website, the largest known Cassandra setup involves over 300 TB of data on over 400 machines.

High Performance Reads and Writes Linear Scalability High Availability

One Good Thing about Cassandra ●  Cassandra makes it easy to scale capacity

Existing cluster nodes running out of space

Start new nodes and let them join

Stored data redistribute automatically

One Bad Thing about Cassandra

cql> create table foo ( id int primary key, customerId int, orderId int, orderValue double);

OK! <Me: I think I’m gonna like Cassandra.>

cql> create index idx_foo_1 on foo (customerId, orderId);

ERROR! - Secondary indexes only support 1 column <Me: I think I just changed my mind. CQL != SQL> <Me: (much later) secondary indexes aren’t that useful>

Today’s Question

How can you use your MySQL knowledge to get up to speed

on Cassandra?

CQL

CQL is not SQL!

What is CQL? ●  Cassandra originally used a Thrift RPC-based API ●  CQL was added in 0.8

○  Simplifies access ○  Smaller learning curve for SQL users

●  So, you’ll feel right at home ○  Create table ○  Data types ○  Insert, update, select, delete

●  But Cassandra isn’t pretending to be relational...

What ISN’T CQL? ●  It’s familiar, and then it’s not!

○  No joins ○  No foreign keys ○  No “not null” ○  No sum(), group(), min(), … ○  Some “ORDER BY” support ○  Single row UPDATE ○  Limited secondary indexing ○  INSERT == UPDATE ■  but it all behaves like REPLACE INTO

The Bottom Line on CQL ●  It’s just a language ●  It’s very similar to SQL ●  Don’t blame CQL

○  What first appears as a “limitation” in Cassandra can also be a strength

○  CQL enables us to get started quickly ○  Try Cassandra 0.7 for a little while…

Commit this to memory... “You do not just throw data into Cassandra and

add later indexes to make it fast.”

Schema Design

“Easy to learn and difficult to master”

- Nolan Bushnell (founder of Atari and Chuck E. Cheese’s Pizza-Time)

Coming from a Relational World? Tradeoffs are hard

Feature RDBMS Cassandra

Single Point of Failure

Cross Datacenter

Linear Scaling

Data Modeling

*source = Patrick McFadin (@PatrickMcMcFadin)

How is My Data “Organized”?

Relational Model Cassandra Model

Database Keyspace

Table Column Family

Primary Key Row Key

Column Name Column Name/Key

Column Value Column Value

What is a BigTable?

http://static.googleusercontent.com/media/research.google.com/en/us/archive/bigtable-osdi06.pdf

Row Key

Column Name

Column Value

Timestamp

TTL

Column Name

Column Value

Timestamp

TTL

Column Name

Column Value

Timestamp

TTL

Column Name

Column Value

Timestamp

TTL

Up to 2 billion!

●  Cassandra uses it for the data model ●  Supports extremely wide rows ●  Row lookup is fast and easily distributed ●  Columns are sorted by name

What About Data Types? ●  Unlike some other NoSQL databases, types

are included via CQL ●  Usual suspects

○  ascii, bigint, blob, boolean, decimal, varchar, …

●  More interesting ○  uuid - global uniqueness ○  timeuuid - global uniqueness, sorted by time portion ○  inet - IPv4 or IPv6 address ○  varint - variable precision integer

How Do I Create a Static Table? CREATE TABLE users (

user varchar,

email varchar,

state varchar,

PRIMARY KEY (user)

);

●  Primary key is hashed for location/placement (more on that later) ○  Meaning you cannot range scan by PK

●  No “not null” or varchar sizing (enforce in applications) ●  Reads (on username) and all writes are easily distributed ●  Schema flexibility without downtime

○  alter table users add column password varchar;

user

email

timestamp ttl

state

timestamp ttl

These rows look and feel familiar.

No Auto-Increment? ●  Auto-increment is extremely difficult in a distributed system ●  Use a natural primary key, if possible

○  Remember, small primary keys aren’t important when denormalizing ●  Or, use uuid / timeuuid

○  Generated in your client applications

CREATE TABLE payments (

id timeuuid,

user varchar,

type varchar,

amount decimal,

PRIMARY KEY (id)

);

What About Secondary Indexes? CREATE TABLE users (

user varchar,

email varchar,

state varchar,

PRIMARY KEY (user));

-- OPTION 1 : create an index

CREATE INDEX idxUBS on users (state);

-- OPTION 2 : create another table (store data twice)

CREATE TABLE usersByState (

state varchar,

user varchar,

PRIMARY KEY (state, user));

Why Not Create Secondary Indexes?

select * from users where state = ‘CA’;

Secondary index ●  Pro : Index is automatically maintained ●  Con : Above query sent to entire cluster (slows everyone down)

Additional table ●  Pro : Above query is directed to single node (important for scalability) ●  Con : Index is manually maintained (insert data twice, which is OK)

General tips ●  Low selectivity is bad - (male/female) ●  Extremely high selectivity is bad - (unique) ●  In general, create additional table and don’t busy entire cluster for reads

How Do I Create a Dynamic Table? CREATE TABLE payments (

user varchar,

id timeuuid,

type varchar,

amount decimal,

PRIMARY KEY (user, id)

);

- Compound PK creates wide row - Remainder of columns are “grouped” - Still 1 “row” per user

user type

timestamp ttl

amount

timestamp ttl

id1 type

timestamp ttl

amount

timestamp ttl

id2

●  Affects how data is stored/organized ●  Still returned in “row” format to CSQL ●  Enables “ORDER BY id” and range query on “id” for “user = ?” queries ●  Remember, BigTables support up to 2 billion columns

What About Relationships? Relational table emp (empName text PK, deptId int, ...);

index empIdx1 on emp (deptId);

table dept (deptId int PK, deptName text);

Cassandra table emp (empName text PK, deptId int, deptName text);

table dept (

deptId int,

deptName text,

empName text,

PRIMARY KEY ((deptId, deptName), empName));

Store department name with employee

Store employee names with department (up to ~2 billion)

1, Accounting FrankJones FredJones SamSmith

What About Time Series Data? CREATE TABLE dashboard (

dashboardId text,

event_time timestamp,

event_value double,

PRIMARY KEY (dashboardId, event_time))

WITH CLUSTERING ORDER BY (event_time DESC);

●  great for “last n”, like dashboards ●  “WITH CLUSTERING ORDER BY ()”

○  Data is stored in given order in BigTable row ○  In this case descending by event_time ○  Easy to access most recent data

How Do I Remove Time Series Data? CREATE TABLE dashboard (

dashboardId text,

event_time timestamp,

event_value double,

PRIMARY KEY (dashboardId, event_time))

WITH CLUSTERING ORDER BY (event_time DESC);

insert into dashboard (dashboardId, event_time, event_value) values (25, now(), 500.12) using ttl 86400;

●  defined at the “data” level ●  data “magically” disappears ●  no more cron jobs

Are There Other Cool Schemas? ●  collections : sets, lists, maps

○  an alternative to making the row “wide” ○  set<text> : ordered by CQL Type comparator ○  list<text> : ordered by insertion ○  map<int,text> : unordered ○  support for 64,000 objects per collection (but don’t go crazy)

create table users (

user varchar,

emails set<text>,

PRIMARY KEY (user));

insert into users (user, emails) values (‘tmcallaghan’,{‘tim@tokutek.com’,’timc@tokutek.com’});

Topic:

Transactions

MySQL Transactions and Isolation mysql> BEGIN; ... mysql> INSERT INTO sample(data) VALUES (“Hello world!”); mysql> INSERT INTO sample(data) VALUES (“Goodbye world!”); ... mysql> COMMIT;

InnoDB creates MVCC view of data; locks updated

rows, commits atomically

MyISAM locks table and commits each row

immediately

Does Cassandra Have Transactions?

“Transactions” includes a lot of things so… No: ●  Updates on different rows are separate, like MyISAM ●  Failed transactions on replicas might create partial

writes (Cassandra does not guarantee clean-up)

Yes: ●  Updates to single rows are atomic and isolated ●  Updates to rows are durable (logged) as well

(Important: Cassandra rows can be “bigger” than MySQL)

How Does Cassandra Handle Locks?

Cassandra uses timestamps instead

create columnfamily sample(id int primary key, data varchar, n int);

insert into sample(id, data, n) values(1, 'hello', 25);

insert into sample(id, data, n) values(2, 'goodbye', 27);

cqlsh:cbench> update sample set data='goodbye!' where id=2;

cqlsh:cbench> select id, writetime(data),writetime(n) from sample;

id | writetime(data) | writetime(n)

----+------------------+------------------

1 | 1396326015674000 | 1396326015674000 ⇐ Updated together 2 | 1396326144783000 | 1396326027160000 ⇐ Updated separately

Timestamps are the same for columns updated at the same time

How Does Cassandra Handle Isolation?

●  Row updates are completely isolated until they are completed

●  Updates can propagate out to replicas at different times

Topic:

Replication and HA

Review of MySQL Replication

How Does Cassandra Replication Work?

●  Cassandra is fully multi-master ●  Updates are allowed at any location ●  Updates can happen even when there is a

partition

db1

db3

db2

db4

Client program issues write

Writes proxied to other instances

Coordinator

How Many Replicas Are There? The number of replicas and how they are

distributed are properties of keyspaces

CREATE KEYSPACE cbench WITH replication = { 'class': 'SimpleStrategy', 'replication_factor': '3' };

Keep 3 copies of data

Strategy class to distribute replicas

MySQL Partitioning •  MySQL partitioning breaks a table into <n> tables

o  “PARTITION” is actually a storage engine •  Tables can be partitioned by hash or range

o  Hash = random distribution o  Range = user controlled distribution (date range)

•  Helpful in “big data” use-cases •  Partitions can usually be dropped efficiently

o  Unlike “delete from table1 where timeField < ’12/31/2012’;”

How Does Cassandra Partition Data? Cassandra splits data by key across hosts

using a “ring” to assign locations

A

B

F

E D

C

G

H

db3 EFGHAB

db4 GHABCD

db2 CDEFGH

db1 ABCDEF

Virtual node D gets ⅛ of hash range

Copy assigned to host db4 by strategy

Partitioning in Action

Replica placement is based on primary key

insert into sample(id, data) values (550e8400-e29b-41d4-a716-446655440000, 'hello!');

Primary Key

Run hash function on value, assign to virtual node, from there to actual hosts

What About Conflicts? ●  Any client can update rows from any location ●  Cassandra resolves conflicts using

timestamps ●  Conflicts are resolved at the cell level ●  The latest timestamp value wins

Id Data Age

352 “bye” 35

UPDATE sample SET data = ‘hello’, age=35 WHERE id=352 UPDATE sample SET data =

‘bye’ WHERE id=352

WIN WIN

LOSE

So Is This Like Galera? ●  Galera uses ACID transactions with

optimistic locking ●  It would accept the first transaction and roll

back the second completely

Id Data Age

352 “bye” 35

UPDATE sample SET data = ‘hello’, age=35 WHERE id=352 UPDATE sample SET data =

‘bye’ WHERE id=352

WIN

WIN

LOSE

How Does “Failover” Work?

●  Cassandra does not have failover ●  If a node fails or disappears, others continue ●  Writes or reads may stop if too many

replicas fail

db1 db2

db4

Client program issues write

Writes proxied to other instances

X

Coordinator

Tunable Consistency

Clients define the level of consistency

cqlsh:cbench> consistency all Consistency level set to ALL. cqlsh:cbench> update rtest set k1=4444 where id=3; Unable to complete request: one or more nodes were unavailable. cqlsh:cbench> select * from cbench.rtest where id=3; Unable to complete request: one or more nodes were unavailable. cqlsh:cbench> consistency quorum Consistency level set to QUORUM. cqlsh:cbench> update rtest set k1=4444 where id=3;

What Happens To Failed Writes? ●  Cassandra has several repair mechanisms

for failures

●  Hinted Handoff - The coordinator remembers the write and replays it when node returns

●  Read Repair - Coordinator for a later read notices that a value is out of sync

●  Node Repair - Run an external process (nodetool) to scan for and fix inconsistencies

What Else is There to Learn? “A lot” would be an understatement, but here

are some topics to consider.

●  You need to “rethink” your data model, so read up, practice, repeat

●  Data consistency is an application problem ●  Storage/Internals : LSM, Compaction,

Tombstones, Bloom Filters

What Should You Do?

Summary 1 We liked… ●  CQL + “Tables” made it easy to get started ●  HA ●  Scaling model

Look [out] for… ●  Don’t bother trying to “port” your MySQL application ●  Query patterns are critical, model around them ●  Pay attention to version when reading docs/blogs

○  Cassandra is quickly evolving (0.7, 1.0, 1.2, 2.0, …)

Summary 2 Highly recommended reading ●  Book: “Practical Cassandra: A Developer’s Approach” ●  Datastax has great docs, http://www.datastax.com/docs ●  Presentations

○  http://www.slideshare.net/jaykumarpatel/cassandra-data-modeling-best-practices

●  Blogs ○  http://planetcassandra.org ○  http://ebaytechblog.com

Questions?

Robert Hodges CEO, Continuent

robert.hodges@continuent.com @continuent

Tim Callaghan VP/Engineering, Tokutek

tim@tokutek.com @tmcallaghan