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N O S Q L I N T R O & C A S S A N D RA 1

N O S Q L I N T R O & C A S S A N D RA

REQUISITE SLIDE – WHO AM I?

- Brian Enochson- Home is the Jersey Shore- SW Engineer who has worked as designer / developer on NOSQL

(Mongo, Cassandra)- Consultant – HBO, ACS, CIBER- Specialize in SW Development, architecture and training

Brian Enochson

brian.enochson@gmail.com

Available for training, consulting, architecture & development.

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REQUISITE SLIDE # 2 – WHAT ARE WE TALKING ABOUT?

• NoSQL Introduction• What brought us here• Types of NoSQL Products• What about Hadoop?• What about Real-Time?• Quick look at MongoDB

• Cassandra Intro & Architecture• Why Cassandra• Architecture• Internals• Development

• Data Modeling Concepts• Old vs. New Way• Basics• Composite Types• Collections• Time Series Data• Counters

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HISTORY OF THE DATABASE

• 1960’s – Hierarchical and Network type (IMS and CODASYL)

• 1970’s – Beginnings of theory behind relational model. Codd

• 1980’s – Rise of the relational model. SQL. E/R Model (Chen)

• 1990’s – Access/Excel and MySQL. ODMS began to appear

• 2000;’s – Two forces; large enterprise and open source. Google and Amazon. CAP Theorem (more on that to come…)

• 2010’s – Immergence of NoSQL as an industry player and viable alternative

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WHY WERE ALTERNATIVES NEEDED

• Developers today are faced with Internet scale• 100,000’s of users• Low cost of storage• Increased processing power• Ability to capture (and need) of millions of events. Caching solves it to an

extent but brings other complexities• Real-time• Need to scale out and not up. (add infinite number of low cost machines,

vs. add a more powerful machine).

• Cost• Let’s not forget for enterprise DB’s Internet scale can become expensive• Open source DB’s may solve license cost, but don’t ignore operational

costs

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A LOT OF DATA

Some facts from http://www.storagenewsletter.com/rubriques/market-reportsresearch/ibm-cmo-study/

Approximately 90 percent of all the real-time information being created today is unstructured data

Every day we create 2.5 quintillion (10 to the 18th) bytes of data (this is 30 zeroes!!)

90 percent of the world's data today has been created in the last two years alone

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RELATIONAL VS. NOSQL

• Relational

• Divide into tables, relate into foreign keys, DB constraints, normalized data, the Interface is SQL

• NoSQL

• Store in schemaless format, redundancy encouraged, application access determines the storage format (your queries).Interface varies and is optimized for the implementation, no forced DB constraints. Tradeoff is often you get eventual consistency.

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TRADEOFFS?

Luckily, due to the large number of compromises made when

attempting to scale their existing relational databases,

these tradeoffs were not so foreign or

distasteful as they might have been.

Greg Burd - https://www.usenix.org/legacy/publications/login/2011-10/openpdfs/Burd.pdf

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3 V’S – DESCRIBING THE BIG DATA PROBLEMDriving force in requiring new technology is often referred to as the “3

V Model”.

• High Volume – amount of data

• High Variety – range of data types and sources

• High Velocity – speed of data in and out

OK, maybe 4 V’s

• Veracity – is all the data applicable to the problem being analyzed.

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NOSQL IS NOT BIG DATA

NoSQL != Big Data

NoSQL products were created to help solve the big data problem.

Big data is a much larger problem than just storage. Analysis tools like Hadoop, messaging systems like Kafka, real time processing engines like Storm and machine learning (Mahout) all help solve the big data problem.

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NOSQL TYPES

Wide Column– Column Family

• Cassandra, HBASE, Amazon SimpleDB

Key Value

• Riak, Redis, DynamoDB, Voldemort, MemcacheDB

Document DB

• MongoDB, CouchDB,

Graph

• Neo4J, OrientDB

Search (also alternatives, normally used with *)

• Lucene, Solr, ElasticSearch

Many many many, many more! (http://nosql-database.org/)

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CHOOSING THE RIGHT ONE…

Choosing the right NoSQL type and eventual product depends on…

Type of Data• One key and a lot of data?• High volume of data?• Storing, media, blobs, • Document oriented?• Tracking relationships?• Combination?• Multi-Datacenter

Type of Access

Volumes of Data (there is big data and there is BIG DATA)

Need Support/Services/Training

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VISUAL GUIDE – USING THE CAP THEOREMHTTP://BLOG.NAHURST.COM/VISUAL-GUIDE-TO-NOSQL-SYSTEMS

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QUICK LOOK AT MONGO

Just so we can compare to Cassandra

• Document Oriented

• Storage format is JSON (actually BSON)

• Replication built in

• Master / slave architecture

• Strong querying support

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MONGO DOCUMENTS

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DETOUR OVER…

LET’S TALK ABOUT CASSANDRA….

OR

C*

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CASSANDRA HISTORY

• Developed At Facebook, based on Google Big Table and Amazon Dynamo **

• Open Sourced in mid 2008

• Apache Project March 2009

• Commercial Support through Datastax (originally known as Riptano, founded 2010)

• Used at Netflix, eBay and many more. Reportedly 300 TB on 400 machines largest installation

• Current version is 2.0.1

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WHY EVEN CONSIDER C*

• Large data sets

• Require high availability

• Multi Data Center

• Require large scaling

• Write heavy applications

• Can design for queries

• Understand tunable consistency and implications (more to come)

• Willing to make the effort upfront for the reward

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SOME BASICS

• ACID

• CAP Theorem

• BASE

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ACID

YOU PROBABLY ALL HAVE HEARD OF ACID

• Atomic – All or None

• Consistency – What is written is valid

• Isolation – One operation at a time

• Durability – Once committed to the DB, it stays

This is the world we have lived in for a long time…

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CAP THEOREM (BREWERS)

Many may have heard this one

CAP stands for Consistency, Availability and Partition Tolerance• Consistency –like the C in ACID. Operation is all or nothing,

• Availability – service is available.

• Partition Tolerance – No failure other than complete network failure causes system not to respond

(REMEMBER VISUAL GUIDE TO SELECTING A NO SQL DATABASE

So.. What does this mean?

** http://www.cs.berkeley.edu/~brewer/cs262b-2004/PODC-keynote.pdf

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YOU CAN ONLY HAVE 2 OF THEM

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Or better said in C* terms you can have Availability and Partition-Tolerant AND Eventual Consistency.

Means eventually all accesses will return the last updated value.

N O S Q L I N T R O & C A S S A N D RA

BASE

But maybe you have not heard this one…

Somewhat contrived but gives CAP Theorem an acronym to use against ACID… Also created by Eric Brewer.

Basically Available – system does guarantee availability, as much as possible.

Soft State – state may change even without input. Required because of eventual consistency

Eventually Consistent – it will become consistent over time.

** Also, as engineers we cannot believe in anything that isn’t an acronym!

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C* - WHAT PROBLEM IS BEING SOLVED?

• Database for modern application requirements.

• Web Scale – massive amounts of data• Scale Out – commodity hardware• Flexible Schema (we will see this how this concept is evolving)• Online admin (add to cluster, load balancing). Simpler operations• CAP Theorem Aware

• Built based on• Amazon Dynamo – Took partition and replication from here **

• Google Bigtable – log structured column family from here ***

** http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html*** http://research.google.com/archive/bigtable.html

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C* BASICS

• No Single Point of Failure – highly available. • Peer to Peer – no master

• Data Center Aware – distributed architecture

• Linear Scaling – just add hardware

• Eventual Consistency, tunable tradeoff between latency and consistency

• Architecture is optimized for writes.

• Can have 2 billion columns!

• Data modeling for reads. Design starts with looking at your queries.

• With CQL became more SQL-Like, but no joins, no subqueries, limited ordering (but very useful)

• Column Names can part of data, e.g. Time Series

Don’t be afraid of denormalized and redundant data for read performance. In fact embrace it! Remember, writes are fast.

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NOTE ABOUT EVENTUAL CONSISTENCY

** Important Term **

Quorum : Q = N / 2 + 1.

We get consistency in a BASE world by satisfying W + R > N

3 obvious ways:

1.W = 1, R = N

2.W = N, R = 1

3.W = Q, R = Q

(N is replication factor, R = read replica count, W = write replica count)

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THE C* DATA MODEL

C* data model is made of these:Column – a name, a value and a timestamp. Applications can use the name

as the data and not use value. (RDBMS like a column).

Row – a collection of columns identified by a unique key. Key is called a partition key (RDBMS like a row).

Column Family – container for an ordered collection rows. Each row is an ordered collection of columns. Each column has a key and maybe a value. (RDBMS like a table). This is also known as a table now in C* terms.

Keyspace – administrative container for CF’s. It is a namespace. Also has a replication strategy – more late. (RDBMS like a DB or schema).

Super Column Family – say what?

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SUPER COLUMN FAMILY

Not recommended, but they exist. Rarely discussed

It is a key, that contains to one or more nested row keys and then these each contain a collection of columns.

Can think of it as a hash table of hash tables that contain columns..

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ARCHITECTURE (CONT.)

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http://www.slideshare.net/gdusbabek/data-modeling-with-cassandra-column-families

N O S Q L I N T R O & C A S S A N D RA

OR CAN ALSO BE VIEWED AS…

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http://www.slideshare.net/gdusbabek/data-modeling-with-cassandra-column-families

N O S Q L I N T R O & C A S S A N D RA

TOKENS

Tokens – partitioner dependent element on the ring.

Each node has a single unique token assigned.

Each node claims a range of tokens that is from its token to token of the previous node on the ring.

Use this formula

Initial_Token= Zero_Indexed_Node_Number * ((2^127) / Number_Of_Nodes)

In cassandra.yaml

initial token=42535295865117307932921825928971026432

** http://blog.milford.io/cassandra-token-calculator/

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C* PARTITIONER

RandomPartitioner – MD5 hash of key is token (128 bit number), gives you even distribution in cluster. Default <= version 1.1

OrderPreservingPartitioner – tokens are UTF-8 strings. Uneven distribution.

Murmur3Partitioner – same functionally as RandomPartitioner, but is 3 – 5 times faster. Uses Murmur3 algorithm. Default >= 1.2

Set in cassandra.yaml

partitioner: org.apache.cassandra.dht.Murmur3Partitioner

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REPLICATION

• Replication is how many copies of each piece of data that should be stored. In C* terms it is Replication Factor or “RF”.

• In C* RF is set at the keyspace level:CREATE KEYSPACE drg_compare WITH replication = {'class':'SimpleStrategy', 'replication_factor':3};

• How the data is replicated is called the Replication Strategy• SimpleStrategy – returns nodes “next” to each other on ring,

Assumes single DC• NetworkTopologyStrategy – for configuring per data center.

Rack and DC’s aware.update keyspace UserProfile with strategy_options=[{DC1:3, DC2:3}];

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SNITCH

• Snitch maps IP’s to racks and data centers.

• Several kinds that are configured in cassandra.yaml. Must be same across the cluster.

SimpleSnitch - does not recognize data center or rack information. Use it for single-data center deployments (or single-zone in public clouds)

PropertyFileSnitch - This snitch uses a user-defined description of the network details located in the property file cassandra-topology.properties. Use this snitch when your node IPs are not uniform or if you have complex replication grouping requirements.

RackInferringSnitch - The RackInferringSnitch infers (assumes) the topology of the network by the octet of the node's IP address. 

EC2* - EC2Snitch, EC2MultiRegionSnitch

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RING TOPOLOGY

When thinking of Cassandra best to think of nodes as part of ring topology, even for multiple DC.

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SimpleStrategy

Using token generation values from before. 4 node cluster. Write value with token 32535295865117307932921825928971026432

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SimpleStrategy #2

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SimpleStrategy #3

With RF of 3 replication works like this:

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NetworkTopologyStrategyUsing LOCAL_QUORUM, allows write to DC #2 to be asynchronous.

Marked as success when writes to 2 of 3 nodes (http://www.datastax.com/dev/blog/deploying-cassandra-across-multiple-data-centers)

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COORDINATOR & CL

• When writing, Coordinator Node will be selected. Selected at write (or read) time. Not a SPF!

• Using Gossip Protocol nodes share information with each other. Who is up, who is down, who is taking which token ranges, etc. Every second, each node shares with 1 to 3 nodes.

• Consistency Level (CL) – says how many nodes must agree before an operation is a success. Set at read or write operation.• ONE – coordinator will wait for one node to ack write (also TWO, THREE). One is

default if none provided.• QUORUM – we saw that before. N / 2 + 1. LOCAL_QUORUM, EACH_QUORUM• ANY – waits for some replicate. If all down, still succeeds. Only for writes.

Doesn’t guarantee it can be read.• ALL– Blocks waiting for all replicas

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ENSURING CONSISTENCY

3 important concepts:

Read Repair - At time of read, inconsistencies are noticed between nodes and replicas are updated. Direct and background. Direct is determined by CL.

Anti-Entropy Node Repair - For data that is not read frequently, or to update data on a node that has been down for a while, the nodetool repair process (also called anti-entropy repair). Builds Merkle trees, compares nodes and does repair.

Hinted Handoff - Writes are always sent to all replicas for the specified row regardless of the consistency level specified by the client. If a node happens to be down at the time of write, its corresponding replicas will save hints about the missed writes, and then handoff the affected rows once the node comes back online. This notification happens is via Gossip. Default 1 hour.

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SUMMARY

C* Provider highly available, distributed, DC aware DB with tuneable consistency out of the box.

A lot of tools at your disposal.

Work close with ops or devops .

Test, test and test again.

Don’t be afraid to use the C* community.

Brian Enochson

brian.enochson@gmail.com

Available for training, consulting, architecture & development.

Thank you!

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