OLTP Benchmark is used to evaluate the performance and cost of transactional database systems. It supports various OLTP workloads and allows evolving mixtures of transactions over time. The benchmark was used to compare the performance per dollar of different Amazon RDS instance sizes for YCSB and Wikipedia workloads. For YCSB, the L instance provided the best cost-performance ratio, while the XL-HM instance was best for Wikipedia. OLTP Benchmark can also smoothly change the transaction mixture during a run to evaluate how systems respond. It provides a standardized way to test OLTP databases and compare cloud database offerings.

1. OLTP
Benchmark
B Y: S A I K AT G A R A I

2. outline
Introduction And Architecture
Workload
Performance vs Cost Comparison
Evolving Workload Mixtures
Conclusion

3. Introduction
What is OLTP?
OLTP is an operational system that supports transaction-oriented applications in a 3-tier
architecture. It administers the day to day transaction of an organization. OLTP is basically focused
on query processing, maintaining data integrity in multi-access environments as well as
effectiveness that is measured by the total number of transactions per second. The full form of
OLTP is Online Transaction Processing.
What is benchmarking?
A Benchmark is the act of running a computer program, a set of programs, or other operations, in
order to assess the relative performance of an object, normally by running a number of standard
tests and trials against it.

4. Benchmark Model

5. OLTPArchitecture :

6. Requirements of OLTP :

7. Scalability: the ability to drive the system under test at high transactional rates (i.e., test for max
throughput);
Fine-Grained Rate Control: the ability to control the rate of requests with great precision;
Mixed and Evolving Workloads: the ability to support mixed workloads, and to change the rate,
composition, and access distribution of the workloads dynamically over time;
Synthetic and Real Data & Workloads: support for both synthetic and real data sets and workloads
(and thus both programmatically-generated and trace-based executions);
Ease of Deployment/Portability: the ability to deploy experimental settings and run experiments
easily on a variety of DBMSs and DBaaSs using an integrated framework;
Extensibility: the ability to extend the set of workloads and systems supported with a minimal
engineering effort (e.g., centralizing SQL dialect localizations);
Lightweight, Fine-Grained Statistics Gathering: the ability to collect detailed statistics of both client-
side activity and server-side resource utilization with minimum impact on the overall performance;

8. Workloads
AuctionMark
Epinions
JPAB
ResourceStresser
SEATS
TATP
TPC-C
Twitter
Wikipedia
YCSB

9. Profile info for the benchmark workloads

10. Performance v/s Cost Comparison
The OLTP-Bench is used to measure the performance-vs-cost ratio of a single
DBaaS provider
Using the YCSB and Wikipedia benchmarks, we used OLTP-Bench to deploy
databases on five different instance sizes on Amazon’s RDS platform

11. Performance v/s Cost Comparison

12. Performance v/s Cost Comparison
We then ran each benchmark separately at its maximum speed for a total of 30
minutes.
 During that time, OLTP-Bench calculates the average maximum sustained
throughput and the 95th percentile latency from the middle 20 minutes of the
experiment’s run.

13. Performance v/s Cost Comparison

14. Performance v/s Cost Comparison
As for the price/performance ratio, the results for YCSB, the L instance yields
the best cost/performance ratio, with good overall throughput and low latency.
As for the price/performance ratio, the results for Wikipedia suggest that the
XL-HM instance is the best choice for this workload. Although the 2XL-HM and
4XL-HM instances provide better performance, the additional cost incurred is
more .

15. Evolving Workload Mixtures
OLTP-Bench’s ability to smoothly evolve the transaction mixture during an
experiment.
Choosing YCSB as our target workload since it is composed of a series of
simple transactions.
Using MySQL we run the system.

16. Representation using charts

17. Comparing DBaaS Providers
Network latency is an important factor for OLTP workloads
In this example all of the workers on virtual machines hosted
in the same datacenter region
The test is done test to ensure fairness between the
DBaaS providers

18. Cost V/s Performance

19. Conclusion
Benchmarking has always been central for high-performance and efficient data
management system design, deployment and maintenance.
The advent of cloud-based data management solutions with their highly shared
resources, non-user controlled system tuning, and novel pricing schemes further
exacerbate the need for careful benchmarking.

20. Thank You