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Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Safe Harbor Statement
The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.
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Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
The Next Generation of In-Memory Database Oracle TimesTen Velocity Scale In-Memory Database
Doug Hood TimesTen Product Manager
November 17, 2016
Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Agenda
Introduction to TimesTen
TimesTen Velocity Scale In-Memory Database
Demo
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1
2
3
Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
20 Years of Extreme Performance
• 1998 First commercial In-Memory RDBMS
• HA Replication • Online Upgrades • Application-tier Cache
for Oracle Database
• PL/SQL and OCI Support • Oracle Clusterware Integration • Cache Grid for Scale Out • ODP .NET Support • BLOB, CLOB, NCLOB data types
• Parallel Replication • In-Memory Analytics • Columnar Compression • Index Advisor • Oracle R Support • In-Memory Star Join • Oracle Golden Gate Integration
Oracle TimesTen – Pure In-Memory Relational Database
• Parallel data import from Oracle Database
• Parallel database restart • Highly concurrent range
indexes • Parallel Replication with
commit order optimization
• Oracle RAC integration • National Language Support • Oracle Data Types support • SQL Developer Integration • Enterprise Manager integration
5
TimesTen 6
TimesTen 7
Pre-Oracle acquisition TimesTen 11g
11.2.1
TimesTen 11g
11.2.2
TimesTen 11.2.2.x
Enhancements
1996 |
2005
2006 |
2008
2009 |
2011
2012|
2013
2014 |
2016
Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Relational Database
– Pure in-memory
– ACID compliant
– Standard SQL
– Entire database in DRAM
Persistent and Recoverable – Database and Transaction logs
persisted on local disk or flash storage
– Replication to standby and DR systems
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Extremely Fast – Microseconds response time
– Very high throughput
Highly Available – Active-Standby and multi-master
replication
– Very high performance parallel replication
– HA and Disaster Recovery
Oracle TimesTen In-Memory Database
Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Low Latency - Microseconds Response Time Performance – Response Time
Millionths of a
Second
Millionths of a
Second
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TPTBM Read and Update E5-2699 v4 @ 2.20GHz
2 socket, 22 cores/socket,
2 threads/core TimesTen 11.2.2.8.0 (100M rows, 17GB)
1.64
5.06
0.00
1.00
2.00
3.00
4.00
5.00
6.00
SELECT Query UPDATE Transaction
Mic
rose
con
ds
Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Performance - Throughput 5.6 Million Transactions Per Second
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TPTBM Mixed Workload
(80%R-10%U-5%I-5%D) E5-2699 v4 @ 2.20GHz
2 socket, 22 cores/socket,
2 threads/core TimesTen 11.2.2.8.0 (100M rows, 17GB)
5,619,403
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
1 2 4 8 12 16 20 24
Tran
sact
ion
s P
er S
eco
nd
Concurrent Processes
5.6 Million Transactions Per Second
Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Most Widely Used Relational In-Memory Database Deployed by Thousands of Companies
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Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Introduction to TimesTen
TimesTen Velocity Scale In-Memory Database
Demo
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1
2
3
Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Oracle TimesTen Velocity Scale In-Memory Database
• Scale-out, shared nothing architecture
• Data location transparency
• Built-in high availability via K-safety
• Elastic scalability
• Easy to deploy and manage
• Easy application development for OLTP workloads
• Supports analytic workloads
• On-premises or Cloud deployment
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Single Image In-Memory Database
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Database Element
• Each Element has its own set of checkpoint files and transaction log files for persistence
• The Element is the smallest unit for database persistence, failure recovery and high availability
Element <n>
Tx Logs
Checkpoints
Database Element
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Unit of Persistence and Recovery
Single Image In-Memory Database
Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Single Database Image
• Database size not limited by memory
• Table data distributed across all elements – All elements are equal
• Connect to any element and access all data – Distributed queries, joins & transactions
• No need to de-normalize data model
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Data Distribution Methods
• Distribute by Hash – Primary key or user-specified columns
– Consistent hash algorithm
– Examples: Customers, Subscribers, Accounts
• Distribute by Reference – Co-locate related data to optimize joins
– Based on FK relationship
– Supports multi-level hierarchy
• Distribute by Duplicate
– Identical copies on all elements
– Useful for reference tables
– Read and join optimization
Customer
Distribute by Hash
Distribute Table Data by Hash, Reference or Duplicate
Element 1
0 David
4 Igor
8 Tim
Element 2
1 Bill
5 Sam
9 Charles
Element 3
2 Olaf
6 Henri
10 Jie
Element 4
3 Chi
7 Simon
11 Chris
Distribute by Reference
Order
1 0 16/6/15
6 8 16/3/22
2 5 16/2/22 5 6 16/5/10 3 3 16/3/1
4 11 16/2/5
phone 100
tablet 200
watch 300
phone 100
tablet 200
watch 300
phone 100
tablet 200
watch 300
phone 100
tablet 200
watch 300
Products
Distribute by Duplicate
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Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
High Availability and Maximum Throughput
• Built-in HA via multiple copies of the data (K-safety)
– Automatically kept in sync
• All replicas are active for reads and writes
– Double the compute capacity
• Transactions can be initiated from and executed on any replica
K-Safety, All Active
A
A
B
B
C
C
D
D
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Database Fault Tolerance – No Application Down Time Provided one entire copy of the database is available
• If multiple elements fail, applications will continue provided there is one complete copy of the database
• Recovery after failure is automatic • If an entire replica set is down,
application can explicitly choose to accept partial results
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Elastic Scalability
Adding and removing DB elements
- Data automatically redistributed to new elements
- Workload automatically uses the new elements
- Connections will start to use new elements
- Throughput increases with added compute resources
Expand and shrink the database based on business needs
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E
E
B
A
C
A
B
C
D
D
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Deployment and Management
• Single point of configuration and deployment
- Management instance(s)
• Powerful command line tools
- ttGridAdmin and ttGridRollout
• Extensive support in SQL Developer as well
- Velocity Scale grid rollout, monitoring and management
- SQL and table editor support
- Data loading
Single point of Control, via command line or SQL Developer
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• Myth 1: You need a NoSQL DB to scale
• Myth 2: You need to abandon ACID txns to scale
• Myth 3: You need to de-normalize [no joins] to scale
• Myth 4: You need to manually shard your DB to scale
• Myth 5: You need to re-write your apps to scale
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• Velocity Scale is proof that these myths are false
• Velocity Scale offers performance without compromise
Common Database Performance Myths
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TimesTen Velocity Scale IMDB vs Cassandra
YCSB N*10M Rows Servers: 12 x X5-2 Clients: 2 x ODA
Network: 10Gbps
YCSB (Yahoo Cloud Service Benchmark) – 50% reads 50% writes [Workload A]
20
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
2x1 4x1 6x1 8x1 10x1 12x1
Op
era
tio
ns
pe
r se
con
d
Configuration (NxK)
Cassandra TimesTen Velocity Scale
Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
TimesTen Velocity Scale IMDB vs Cassandra
YCSB N*10M Rows Servers: 12 x X5-2 Clients: 2 x ODA
Network: 10Gbps
YCSB (Yahoo Cloud Service Benchmark) – 95% reads 5% writes [Workload B]
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1x2 2x2 3x2 4x2 5x2 6x2
Op
era
tio
ns
pe
r se
con
d
Configuration (NxK)
Cassandra TimesTen Velocity Scale
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YCSB N*10M Rows Servers: 12 x X5-2 Clients: 2 x ODA
Network: 10Gbps 0
1000
2000
3000
4000
5000
6000
7000
8000
9000
2x1 4x1 6x1 8x1 10x1 12x1
Late
ncy
(u
s)
Average Read Latency
TimesTen
Cassandra
TimesTen Velocity Scale IMDB vs Cassandra YCSB (Yahoo Cloud Service Benchmark) – 95% reads 5% writes [Workload B]
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VelociScale DB – Recalc priority Flow
0
10
20
30
40
50
60
Bryce 1x2 2x2 3x2 4x2 5x2
Complex Update Txn (9 table join, hundreds of millions of rows)
Late
ncy
(Se
c)
Number of x5-2 machines
1x1 11.2.2.8
Latency - smaller is better
TimesTen 11.2.2.8 TimesTen VelociScale DB
Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Application Development
SQL
PL/SQL
Languages C/C++/C#
ODBC
ttClasses
OCI, Pro*C
ODP.NET
J2EE App Servers OR Mapping Java/JDBC
Oracle Confidential - Highly Restricted
Coming soon:
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Developing Applications for Velocity Scale - Existing RDBMS and TimesTen skills apply
- Same SQL, same APIs, same languages
- No application code changes needed for existing TimesTen applications
- Need to consider table distribution types
- Hash may not be optimal for all cases
- Use Index Advisor and MVs if SQL joins are the bottleneck
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Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Introduction to TimesTen
TimesTen Velocity Scale In-Memory Database
Demo
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1
2
3
Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |
Oracle TimesTen Velocity Scale In-Memory Database • Scale-out, shared nothing architecture with single database image
• Data location transparency
• Built-in high availability via K-safety
• Elastic scale-out
• Easy to deploy and manage
• Easy application development for OLTP and Analytic workloads
• On-premises or Cloud deployment
• You can have all of elastic scalability, HA, high performance, ACID transactions, full consistency and full featured SQL
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Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted 29
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