Cassandra, Modeling and Availability at AMUG

Conceptual Modeling Differences From A RDBMSMatthew F. Dennis, DataStax // @mdennis

Austin MySQL User GroupJanuary 11, 2012

Cassandra Is Not Relationalget out of the relational mindset when working

with Cassandra (or really any NoSQL DB)

Work Backwards From QueriesThink in terms of queries, not in terms of

normalizing the data; in fact, you often want to denormalize (already common in the data

warehousing world, even in RDBMS)

OK great, but how do I do that?Well, you need to know how Cassandra Models

Data (e.g. Google Big Table)

research.google.com/archive/bigtable-osdi06.pdf

Go Read It!

In Cassandra:

➔data is organized into Keyspaces (usually one per app)

➔each Keyspace can have multiple Column Families

➔each Column Family can have many Rows

➔each Row has a Row Key and a variable number of Columns

➔each Column consists of a Name, Value and Timestamp

In Cassandra, Keyspaces:

➔are similar in concept to a “database” in some RDBMs

➔are stored in separate directories on disk

➔are usually one-one with applications

➔are usually the administrative unit for things related to ops

➔contain multiple column families

In Cassandra, In Keyspaces, Column Famlies:

➔are similar in concept to a “table” in most RDBMs

➔are stored in separate files on disk (many per CF)

➔are usually approximately one-one with query type

➔are usually the administrative unit for things related to your data

➔can contain many (~billion* per node) rows

* for a good sized node(you can always add nodes)

In Cassandra, In Keyspaces, In Column Families ...

Rows

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Row Keys




Columns

Column Names




Column Values







Rows Are Randomly Ordered(if using the RandomPartitioner)




Columns Are Ordered by Name(by a configurable comparator)

Columns are ordered because doing so allows very efficient

implementations of useful and common operations

(e.g. merge joins)

In particular, within a row I can find given columns by name very quickly (ordered names => log(n)

binary search).

More importantly, I can query for a slice between a start and end

RK ts0 ts1 ... ... tsM ... ... ... ... tsN ... ... ... ... ...

start end

Row Key

Why does that matter?Because columns within a row aren't static!

INTC ts0: $25.20 ts1: $25.25 ...

AMR ts0: $6.20 ts9: $0.26 ...

CRDS ts0: $1.05 ts5: $6.82 ...

Columns Are Ordered by Name(in this case by a TimeUUID Comparator)

The Column Name Can Be Part of Your Data

Turns Out That Pattern Comes Up A Lot

➔stock ticks➔event logs➔ad clicks/views➔sensor records➔access/error logs➔plane/truck/person/”entity” locations➔…

OK, but I can do that in SQLNot efficiently at scale, at least not easily ...

ticker timestamp bid ask ...

AMR ts0 ... ... ...

... ... ... ... ...

CRDS ts0 ... ... ...

... ... ... ... ...

... ts0 ... ... ...

AMR ts1 ... ... ...

... ... ... ... ...

... ... ... ... ...

… ts1 ... ... ...

AMR ts2 ... ... ...

... ts2 ... ... ...

Data I Care About

How it Looks In a RDBMS

ticker timestamp bid ask ...

AMR ts0 ... ... ...

AMR ts1 ... ... ...

AMR ts2 ... ... ...

... ts2 ... ... ...

Disk Seeks

How it Looks In a RDBMS

Larger Than Your Page Size

Larger Than Your Page Size

OK, but what about ...

➔PostgreSQL Cluster Command?

➔MySQL Cluster Indexes?

➔Oracle Index Organized Tables?

➔SQLServer Clustered Index?

OK, but what about ...➔PostgreSQL Cluster Using?

➔MySQL [InnoDB] Cluster Indexes?

➔Oracle Index Organized Table?

➔SQLServer Clustered Index? (seriously, who uses SQLServer?!)

Meh ...

The on-disk management of that clustering results in tons of IO …

In the case of PostgreSQL:

➔clustering is a one time operation (implies you must periodically rewrite the entire table)

➔new data is *not* written in clustered order (which is often the data you care most about)

OK, so just partition the tables ...

Not a bad idea, except in MySQL there is a limit of 1024 partitions and generally less if using NDB

(you should probably still do it if using MySQL though)

http://dev.mysql.com/doc/refman/5.5/en/partitioning-limitations.html

OK fine, I agree storing data that is queried together on disk together is a good thing but

what's that have to do with modeling?

RK ts0 ts1 ... ... tsM ... ... ... ... tsN ... ... ... ... ...

Read Precisely My Data *

Seek To Here

* more on some caveats later

Well, that's what is meant by “work backwards from your queries” or “think in terms of queries”

(NB: this concept, in general, applies to RDBMSat scale as well; it is not specific to Cassandra)

An Example From Fraud Detection

To calculate risk it is common to need to know all the emails, destinations, origins, devices, locations, phone

numbers, et cetera ever used for the account in question

id name ...

1 guy ...

2 gal ...

... ... ...

id email ...

100 guy@ ...

200 gal@ ...

... ... ...

id dest ...

15 USA ...

25 Finland ...

... ... ...

id device ...

1000 0xdead ...

2000 0xb33f ...

... ... ...

id origin ...

150 USA ...

250 Nigeria ...

... ... ...

In a normalized model that usually translates to a table for each type of entity being tracked

The problem is that at scale that also means a disk seek for each one …

(even for perfect IOT et al if across multiple tables)

➔Previous emails? That's a seek …➔Previous devices? That's a seek …➔Previous destinations? That's a seek ...

But In Cassandra I Store The Data I Query Together On Disk Together

(remember, column names need not be static)

acctY ... ... ... ... ... ... ...

acctX dest21 dev2 dev7 email9 orig4 ...

acctZ ... ... ... ... ... ... ...

Data I Care About

email:[email protected] = dateEmailWasLastUsed

Column Name Column Value

email3

Don't treat Cassandra (or any DB) as a black box

➔Understand how your DBs (and data structures) work

➔Understand the building blocks they provide

➔Understand the work complexity (“big O”) of queries

➔For data sets > memory, goal is to minimize seeks *

* on a related note, SSDs are awesome

Q?(then brief intermission)

Availability Has Many Levels

➔ Component Failure (disk)

➔ Machine Failure (NIC, cpu, power supply)

➔ Site Failure (UPS, power grid, tornado)

➔ Political Failure (war, coup)

The Common Theme In The Solutions?

Replication

Replication In Cassandra Follows The Dynamo Model *

http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html

Read It!

t3 t1

t2

t0

Every Node Has A Token0 - 2^127

t0 < t1 < t2 < t3 < 2^127

t3 t1

t2

t0

Row Key Determines Node(s)

MD5(RK) => T

t3 < T < 2^127

t3 t1

t2

t0

Row Key Determines Node

MD5(RK) => T

t3 < T < 2^127

First Replica

t3 t1

t2

t0

Walk The Ring To Find Subsequent Replicas *

MD5(RK) => T

t3 < T < 2^127

First Replica

Second Replica

* by default

t3 t1

t2

t0

Writes Happen In Parallel To All Replicas

First Replica

Second Replica

client

RK= ...

Coordinator(not a master)

RK= ...

RK= ...

t3 t1

t2

t0

Some Or All Replicas Respond

First Replica

Second Replica

client

RK= ...

Coordinator Waits For Ack(s)From Destination Node(s)

“ok”

“ok”

X

t3 t1

t2

t0

The Coordinator Responds To Client

First Replica

Second Replica

client

“ok”

Coordinator Waits For Ack(s)From Destination Node(s)

“ok”

“ok”

X

What Nodes Can Be A Coordinator?

The coordinator for any given read or write is really just whatever node the client connected to for that request

any node for any request at any time

How Many Replicas Does The Coordinator Wait For?

➔configurable, per query

➔ONE / QUORUM are the most common (more on this in a moment)

Writing At CL.One

t3 t1

t2

t0First Replica

Second Replica

client

Third Replica

Wait For At Least One Node(eventually all nodes get updates)

X

Writing At CL.One

t3 t1

t2

t0First Replica

Second Replica

client

Third Replica

Wait For At Least One Node(eventually all nodes get updates)

X

“ok”

“ok”

Reading At CL.One

t3 t1

t2

t0First Replica

Second Replica

client

Third Replica

Wait For At Least One Node(so you might read stale data)

X

Reading At CL.One

t3 t1

t2

t0First Replica

Second Replica

client

Third Replica

Wait For At Least One Node(so you might read stale data)

X

“old”“old”

Writing At CL.Quorum

t3 t1

t2

t0First Replica

Second Replica

client

Third Replica

Wait For Majority Of Nodes(eventually all nodes get updates)

X

Writing At CL.Quorum

t3 t1

t2

t0First Replica

Second Replica

client

Third Replica

Wait For Majority Of Nodes(eventually all nodes get updates)

X

“ok”

“ok”

“ok”

Reading At CL.Quorum

t3 t1

t2

t0First Replica

Second Replica

client

Third Replica

Wait For Majority Of Nodes(majority => overlap => consistent)

X


t3 t1

t2

t0First Replica

Second Replica

client

Third Replica

Wait For Majority Of Nodes(majority => overlap => consistent)

X“ok”

“ok”

“old”coordinator chooses client response based on client supplied per column TS


t3 t1

t2

t0First Replica

Second Replica

client

Third Replica

Read Repair Updates Stale Nodes

X

“current”

Already Has Response

t3

t0

On A Side Note, A Lost Response

“ok”

X

t3

t0

Is The Same As A Lost Request

RK = ...X

* In Regards To Meeting Consistency

t3

t0

Which Is The Same As A Failed/Slow Node

RK = ...

X

* In Regards To Meeting Consistency

In fact, it is actually impossible for the originator to reliably distinguish between the 3

One More Important Piece:

writes are idempotent *

* except with the counter API, but if you want that it can be done

Why is that important?

It means we can replay/retry writes, even late and/or out of order, and get the same results

➔After/during node failures

➔After/during network partitions

➔After/during upgrades

In other words you can concurrently issue conflicting updates to two different nodes while

those nodes have no communication between them

Which is important because ...

Availability Has Many Levels

➔ Component Failure (disk)

➔ Machine Failure (NIC, cpu, power supply)

➔ Site Failure (UPS, power grid, tornado)

➔ Political Failure (war, coup)

If you care about global availability you must serve reads and writes from multiple data centers

There is no way around this

Q?Conceptual Modeling Differences From A RDBMS

Matthew F. Dennis, DataStax // @mdennis

A Brief Rant On Query Planners, Garbage Collectors, Virtual Memory, Automatic Transmissions and Data Structures

Cassandra, Modeling and Availability at AMUG

Technology

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