Scalability: Rdbms Vs Other Data Stores

RDBMS vs. Other Data Stores forScalability
ramki.g@directi.com
TechTalk 2009, IIIT Hyderabad

Scalability
Increase Resources  Increase Performance (Linearly)
Performance?
Latency, Capacity, Throughput
Vertical Scalability (Scaling Up)
Divide the functionality
Horizontal Scalability (Scaling Out)
Divide the data

Relational Database
Table, Row, Column
Set, Item, Property

Relational Theory
Selection: SELECT
Filter: WHERE
Join: JOIN, LEFT JOIN,RIGHT JOIN
Correlation: SELECT a FROM A WHERE A.b IN (SELECT b FROM B WHERE b.a > a)

Relational Theory
Aggregation
Set Operators
Union, Intersection, Minus
Group By
MAX, MIN, SUM, AVG

Transactions: Atomicity
Transaction Level
Entire Logical operations is a transaction
Multiple statements
Statement level
Each statement is either successful or not, no partial success
Multiple records
Record Level
All modifications to a record are successful or not

Transactions: Consistency
Integrity Constraints
Referential Integrity

Transactions: Isolation Levels
Serializable
A definite order of mutations/transactions is possible to arrive to state B from state A
Repeatable Read
Any data read by a transaction will remain so till transaction is complete
Non Repeatable Read aka Read Committed
Two reads within a transaction may give different results
Dirty Read
A transaction might read data which might then be rolledback

RDBMS Luxuries
Multiple Indexes
Auto Increments/Sequences
Triggers

Scalability in RDBMS
Replication
Read Replication (Master-Slave)
Read Write Replication (Master-Master)
Cluster
Distributed Transaction
Two-phase commits

Scalability Impediments
Performance
Sub-Queries/Correlation, Joins, Aggregates,
Referential Integrity constraints
Basic Guarantee
Consistency
Availability

CAP?
Conjecture: Distributed systems cannot ensure all three of the following properties at once
Consistency The client perceives that a set of operations has occurred all at once.
Availability Every operation must terminate in an intended response.
Partition tolerance Operations will complete, even if individual components are unavailable.

ACID to BASE
Basically Available - system seems to work all the time
Soft State - it doesn't have to be consistent all the time
Eventually Consistent - becomes consistent at some later time

BASE: An Example
BEGIN Transaction
INSERT INTO ORDER( oid, timestamp, customer)
FOREACH item IN itemList
INSERT INTO ORDER_ITEM ( oid, item.id, item.quantity, item.unitprice)
//UPDATE INVENTORY SET quantity=quantity- item.quantityWHERE item = item.id
COMMIT
END Transaction
Assume Each statement is queued for execution
You will get COMMIT success

Alternate Implementations
BigTable – Google – CP
Hbase – Apache – CP
HyperTable – Community - CP
Dynamo – Amazon – AP
SimpleDB– Amazon - AP
Voldemort – LinkedIn – AP
Cassandra – Facebook– AP
MemcacheDB - community – CP/AP

Data Models
Key/Value Pairs
Dynamo, MemcacheDB, Voldemort
Row-Column
BigTable, Casandra, SimpleDB, Hypertable, Hbase

Programming Models
// Open the table
Table *T = OpenOrDie("/bigtable/web/webtable");
// Write a new anchor and delete an old anchor
RowMutation r1(T, "com.cnn.www");
r1.Set("anchor:www.c-span.org", "CNN");
r1.Delete("anchor:www.abc.com");
Operation op;
Apply(&op, &r1);

BigTable: Consistent yet Infinitely Scalable
Single Master
B+ tree based data distribution

BigTable: Transactions
Enities and Entity Groups
Invoice
Invoice Item
Delivery Note

Dynamo: Highly available and Infinitely Scalable
Consistent Hashing
Peer to Peer Distributed
Gossip based member discovery

RDBMS or Other?
Nature of Business
Maturity of the Product
Cost of Adoption
Maturity of the alternative Datastores

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Scalability: Rdbms Vs Other Data Stores

by Ramki Gaddipati

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