Indic threads pune12-nosql now and path ahead

NoSQL: Now and Path Ahead

Shubham Kumar Srivastava

MakeMyTrip

Who am I

3

Abstract

What and Why : NoSql

Fundamentals

Use Case

Challenges

Path Ahead

.

What is NoSql

Database which does not adhere to the traditional relational database management system (RDMS) structure .

Why NoSql

Scalability and Performance

Cost

Data Modeling

Why NoSql : Motives and Drivers

Scalability and Performance

Horizontal scalability better than Vertical

Hardware getting cheaper and processing power increasing

Less Operational complexity as against RDBMS solutions.

In most of the solutions you get automatic sharding etc as default .

Why NoSql : Motives and Drivers contd..

Why NoSql : Motives and Drivers contd..

Why NoSql : Motives and Drivers contd.. Cost

Scale(as with NoSql) with Hefty Cost

Commodity hardware, software versions, upgrades, maintenance.

This brought organizations look out for alternatives and the need for a cost effective scale out option.

Why NoSql : Motives and Drivers contd.. Data ModelingSQL has been for

Concurreny,Consistency,Integrity

For Summations,Aggregations,Grouping’s

Schema Says: What all Do I answer ??

Why NoSql : Motives and Drivers contd.. Data Modeling

A plain key-value store is very powerful and fit the max use cases for a NoSQL solution

Hierarchical or graph-like data modelling and processing.

Values like maps of maps of maps.

Document Databases which even store arbitrary complex objects.

Document based indexing data store’s are a huge success.

Why NoSql : Motives and Drivers contd..At times SW apps are not limited to these constraints . This lead to data models like

Key/Value Store :

Redis,MemcacheDb/Voldemort etc.

Wide Column Store / Column Families : Cassandra/Hadoop(Hbase)/Hypertable/Cloudera etc.

Document Based Store’s :

Solr/Lucene/MongoDb/CouchDb/TerraStore etc.

Graph Data Store :

Neo4J/GraphBase/FlockDb etc.

Why NoSql : Motives and Drivers contd..

Schema Says: What are the questions

Data modeling is based on the set of Queries

Exploit De-normalization Duplication

Use Aggregates

Manage Joins with App + Aggregation + DeNormalization etc.

Why NoSql : Motives and Drivers contd..

Some Fanda-mentals

CAP Theorem

At the most only two properties of the three in a shared/distributed system can be satisfied.

Consistency

Availability

Tolerance to Network Partitions

CAP : Pictorially

Explanation

Use case: Scaling Web Apps

Critical fact’s : • Network outages are common • Customer shopping carts, email search, social network

queries—can tolerate stale data

How: Compromise on Consistency in-order to remain available vs disrupt user service at outages.

Rather than requiring consistency after every transaction, it is enough for the database to eventually be in a consistent state.

Brewer’s CAP theorem says you have no choice if you want to scale up.

Explanation

Explanation contd..

Sharp Contrast : High Speed Financial Application

Highly Transactional

Consistent

Automated

Can’t live with Eventual consistency

ACID vs BASE ACIDAtomic: Everything in a transaction succeeds or

the entire transaction is rolled back.

Consistent: A transaction cannot leave the database in an inconsistent state.

Isolated: Transactions cannot interfere with each other.

Durable: Completed transactions persist, even when servers restart etc.

Some Fanda-mentals cont..

BASEBasic Availability

Soft-state

Eventual consistency

Consistent Hashing

Common way to load balance .

The machine chosen to cache object o will be:

hash(o) mod n n:total number of machines

Consistent Hashing contd..

Adding a machine to the cache means hash(o) mod (n + 1)

Removing a machine to the cache means

hash(o) mod (n - 1)

Result on any above: Disaster

Swamped machines with redistribution

Consistent Hashing contd..

Commonly, a hash function(e.g MD5 hash) will

map a value into a 128-bit key, 0~2^127-1(or 32 bit even as given next) .

Consistent Hashing contd..

Consistent Hashing contd.. Both Key and Machine hashed with the same function

Consistent Hashing contd.. Adding a Node

Consistent Hashing contd.. Removing a Node

Use Case and NoSQL Solution

Problem:

Need to store bookings per day of all hotels . Queries centered around city and regions.

Hotel count : 1 Million

Date Range : Now to next 365 *2 Days

NoSQL: Path Ahead

ACID equivalence(Neo4J,CouchDb etc)

Transaction Support

Atomicity

MVCC

NoSQL: Path Ahead contd..

Possible Solution

Work with SQL Db w.r.t Creation/Updation etc.

Archive the data in NoSQL for query/analysis etc.

NoSQL: Path Ahead contd..

Enterprise Adoption and Challenges

NoSQL looks good for Unstructured data largely

SQL is the best choice for a broad range of traditional workloads.

NoSQL: Path Ahead contd..

NoSQL: Path Ahead contd..

Shout out loud Hybrid ACID + BASE They are not alternatives but

supplements

NoSQL: Path Ahead contd.. Maturity

Support

Skillset and Administration/Operation

Analytics and BI support

NoSQL: Path Ahead contd..

Q & A

References Nancy Lynch and Seth Gilbert, “Brewer's conjecture and the feasibility of consistent,

available, partition-tolerant web services”, ACM SIGACT News, Volume 33 Issue 2 (2002), pg. 51-59.

Brewer's CAP Theorem", julianbrowne.com, Retrieved 02-Mar-2010

Brewers CAP theorem on distributed systems", royans.net

CAP Twelve Years Later: How the "Rules" Have Changed on-line resource

E. Brewer, "Towards Robust Distributed Systems," Proc. 19th Ann. ACM Symp.Principles of Distributed Computing (PODC 00), ACM, 2000, pp. 7-10; on-line resource

D. Abadi, "Problems with CAP, and Yahoo’s Little Known NoSQL System," DBMS Musings, blog, 23 Apr. 2010; on-line resource.

C. Hale, "You Can’t Sacrifice Partition Tolerance," 7 Oct. 2010; on-line resource.

Facebook: Scaling Out on-line resource.

Gemstone : The Hardest Problems In Data Management on-line resource

The Log-Structured Merge-Tree (Research Paper)

CodeProject : Consistent Hashing on-line resource

HighlyScalable : NoSQL Data Modeling Techniques on-line resource

eBay Tech Blog :Cassandra Data Modeling Best Practices on-line resource

John D Cook : Acid Vs Base on-line resource

Merkle Trees

Phy-Accural Faliover Detaection (Research Paper)

Backup Slides

Better than the Original 1

Document Based DataStore{

_id : ObjectId("4e77bb3b8a3e000000004f7a"),

when : Date("2011-09-19T02:10:11.3Z",

author : "alex",

title : "No Free Lunch",

text : "This is the text of the post. It could be very long.",

tags : [ "business", "ramblings" ],

votes : 5,

voters : [ "jane", "joe", "spencer", "phyllis", "li" ],

comments : [

{ who : "jane", when : Date("2011-09-19T04:00:10.112Z"),

comment : "I agree." },

{ who : "meghan", when : Date("2011-09-20T14:36:06.958Z"),

comment : "You must be joking. etc etc ..." }

]

}

User and Items

User and Items : Option 1

User and Items : Option 2

User and Items : Option 3

User and Items : Option 4

Cassandra CF

Cassandra SuperCF

Use Case 1Ecommerce Site

Problem : Record User Preferences e.g : Location,IP,Currency selected, Source of Traffic, Multiple other dynamic values

Solution : In a CF based structure keep it simple

UserId_Key: Pref2_Name:Value1,Pref2_Name:Value2,….PrefN_Name:ValueN

Use Case 1RowKey: 1350136093705_6501082438199894

=> (column=1350136093764, value=-3242432#911167901131523, timestamp=1350136093766000)

=> (column=1350283322499, value=GOI#200701231712126570, timestamp=1350283322502001)

=> (column=1350283566051, value=GOI#200703221605283033, timestamp=1350283566054001)

=> (column=1350749595676, value=GOI#200805261514037199, timestamp=1350749595677001)

(column=1350785230322, value=BOM#200701251747233158, timestamp=1350785230324001)

RowKey: 1354499614310_10861558002828044

=> (column=1354499614368, value=TRV#201104071059204768, timestamp=1354499614370000, ttl=1728000)

-------------------

RowKey: 1349760150553_6114662943774777

=> (column=1349760152066, value=BLR#200802111324575807, timestamp=1349760152068001)

-------------------

RowKey: 1349805109805_6167423558533191

=> (column=1349805111833, value=TRV#312254274337517, timestamp=1349805111835001)

-------------------

RowKey: 1354435656227_7908056941568359

=> (column=1354435656367, value=IDR#200701211254519381, timestamp=1354435656369000, ttl=1728000)

-------------------

RowKey: 1347648097261_15570089270962881

=> (column=1347648097304, value=DEL#201101192008115545, timestamp=1347648097307000)

Use Case 1 Get

private Map<String, String> getPrerences(Keyspace keySpace, String userId, String... prefernceNames) throws IOException, CharacterCodingException {

SliceQuery<String, String, String> rsq = HFactory.createSliceQuery(keySpace, StringSerializer.get(), StringSerializer.get(), StringSerializer.get());

rsq.setColumnFamily(USER_PREFERENCE);

rsq.setKey(userId);

rsq.setColumnNames(prefernceNames);

QueryResult<ColumnSlice<String, String>> orows = rsq.execute();

Map<String, String> preferenceMap = new LinkedHashMap<String, String>();

for (HColumn<String, String> column : orows.get().getColumns()) {

preferenceMap.put(column.getName(), column.getValue());

}

return preferenceMap;

}

Use Case 1 Save

Mutator<String> m = HFactory.createMutator(keySpace, StringSerializer.get());

HColumn<String, String> userPrefrences = HFactory.createColumn(colkey, colvalue, StringSerializer.get(), StringSerializer.get());

userPrefrences.setTtl(ttlUserPrefrences);

m.addInsertion(rowkey, USER_PREFERENCE, userPrefrences);

m.execute();

Use Case 2

Online Travel Site

Problem: Need to know different metrics for a city hotels e.g.:

Hotels booked in last X TimeHotels Last viewed in Y TimeHotels Left with Z Inventory

Use Case 2RowKey: 2d323436353731

=> (super_column=911167901297486,

(column=6c6173747669657765646d657373616765, value=VIEWED#Last viewed 23 hour(s) ago., timestamp=1354962852610000)

column=6c6173747669657765646d657373616762, value=Inventory#20 , timestamp=1354962852610000,

column=6c6173747669657765646d657373616769, value=Bookings#8 , timestamp=135496282610000

)

-------------------

RowKey: 58524f

=> (super_column=200903041759196196,

(column=6c617374626f6f6b65646d657373616765, value=Booked#Last booked 1 day(s) ago., timestamp=1347781187842000)

(column=6c6173747669657765646d657373616765, value=VIEWED#Last viewed 2 hours ago., timestamp=1347707080147000))

=> (super_column=200903041848352230,

(column=6c6173747669657765646d657373616765, value=VIEWED#Last viewed 1 day(s) ago., timestamp=1347266107708000))

Use Case 2SuperSliceQuery<String, String, String, String> superQuery = HFactory.createSuperSliceQuery(getKeySpace(),

StringSerializer.get(), StringSerializer.get(),

StringSerializer.get(), StringSerializer.get());

superQuery.setColumnFamily(SUPER_SOCIAL_MESSAGE).setKey(cityCode);

QueryResult<SuperSlice<String, String, String>> result = superQuery.execute();

List<HSuperColumn<String, String, String>> superColumns = result.get().getSuperColumns();

if (superColumns != null) {

for (HSuperColumn<String, String, String> superColumn : superColumns) {

Map<String, String> messages = new HashMap<String, String>();

List<HColumn<String, String>> columns = superColumn.getColumns();

if (columns != null) {

for (HColumn<String, String> column : columns) {

messages.put(column.getName(), column.getValue());

}

}

/* The equivalent doc *\

document.addField(superColumn.getName(), messages);

documents.add(document);

}

}

Pig Script : MR <document>

<pigscript start="-16" end="-43200" start1="-1441" end1="-10080" start2="0" end2="-15" start3="0" end3="-1440">

<comment>Delete All Messages</comment>

<query><![CDATA[rows0 = LOAD 'cassandra://LH/HotelMessage' USING com.mmt.solr.hotels.cassandra.CassandraStorage() as (key:chararray, cols:bag{T:tuple(name:chararray, value:chararray) } );]]></query>

<query><![CDATA[cols0 = FOREACH rows0 GENERATE key as key,flatten($1) as (name:chararray, value:chararray);]]></query>

<query><![CDATA[cols0 = FOREACH rows0 GENERATE key as key,flatten($1) as (name:chararray, value:chararray);]]></query>

<query><![CDATA[userhotel0 = FOREACH cols0 GENERATE key as key,com.mmt.solr.hotels.cassandra.ByteBufferToString($1) as name,com.mmt.solr.hotels.cassandra.ByteBufferToString($2) as value;]]></query>

<query><![CDATA[uriCounts0 = FOREACH userhotel0 GENERATE key as citycode,com.mmt.solr.hotels.cassandra.ToBag(TOTUPLE(name,null));]]></query>

<comment>Last Viewed start 15 minutes to 30 days ago</comment>

<query><![CDATA[rows = LOAD 'cassandra://LH/LastViewedHotels?slice_start=#start&slice_end=#end&limit=1024&reversed=true' USING com.mmt.solr.hotels.cassandra.CassandraStorage() as (key:chararray, cols:bag{T:tuple(name:long, value:chararray) } );]]></query>

<query><![CDATA[cols = FOREACH rows GENERATE key as key,flatten($1) as (name:long, value:chararray);]]></query>

<query><![CDATA[userhotel = FOREACH cols GENERATE key as key,com.mmt.solr.hotels.cassandra.LongToHours($1) as name,com.mmt.solr.hotels.cassandra.ByteBufferToString($2) as value;]]></query>

<query><![CDATA[userhotelByCity = FOREACH userhotel GENERATE key as key,flatten($1) as name,flatten(org.apache.pig.piggybank.evaluation.string.Split(value,'#',2)) as (citycode:chararray,hotelid:chararray);]]></query>

<query><![CDATA[groupByhotels = GROUP userhotelByCity BY hotelid;]]></query>

<query><![CDATA[uriCounts = FOREACH groupByhotels { D = LIMIT userhotelByCity 1;

GENERATE flatten(D.citycode) as citycode,com.mmt.solr.hotels.cassandra.ToBag(

TOTUPLE(group,com.mmt.solr.hotels.cassandra.StringAppend('VIEWED#Last viewed ',D.name,' ago.')));

};]]></query>

<comment>Last Booked 1 to 8 days ago</comment>

<query><![CDATA[rows1 = LOAD 'cassandra://LH/BookedHotels?slice_start=#startA&slice_end=#endA&limit=1024&reversed=true' USING com.mmt.solr.hotels.cassandra.CassandraStorage() as (key:chararray, cols:bag{T:tuple(name:long, value:chararray) } );]]></query>

<query><![CDATA[cols1 = FOREACH rows1 GENERATE key as key,flatten($1) as (name:long, value:chararray);]]></query>

<query><![CDATA[userhotel1 = FOREACH cols1 GENERATE key as key,com.mmt.solr.hotels.cassandra.LongToHours($1) as name,com.mmt.solr.hotels.cassandra.ByteBufferToString($2) as value;]]></query>

<query><![CDATA[userhotelByCity1 = FOREACH userhotel1 GENERATE key as key,flatten($1) as name,flatten(org.apache.pig.piggybank.evaluation.string.Split(value,'#',2)) as (citycode:chararray,hotelid:chararray);]]></query>

<query><![CDATA[groupByhotels1 = GROUP userhotelByCity1 BY hotelid;]]></query>

<query><![CDATA[uriCounts1 = FOREACH groupByhotels1 { D = LIMIT userhotelByCity1 1;

GENERATE flatten(D.citycode) as citycode,com.mmt.solr.hotels.cassandra.ToBag(

TOTUPLE(group,com.mmt.solr.hotels.cassandra.StringAppend('Booked#Last booked ',D.name,' ago.')));

};]]></query>

Criteria's to Evaluate NoSQL Solutions

Internal partitioning

Automated flexible data distribution

Hot swappable nodes

Replication-style

Automated failover strategy