Presentation of a decent paper from Jim Grays Lab

1. CAN THE ELEPHANTS HANDLE
THE NO-SQL ONSLAUGHT?
AUNG THU RHA HEIN
G5537871

2. OUTLINE
 Introduction
 Background
 Evaluation
 Traditional DSS Workload: Hive vs PDW
 Modern OLTP Workload: MongoDB vs SQL Server
 Discussion & Conclusion

3. INTRODUCTION
 Motivation
How does the performance and scalability of RDBMs solutions compare
to the NoSQL systems?
 Proposition
compare MongoDB(AS/CS) with SQL Server and Hive with SQL PWD,
and analyze the performance and scalability aspects on two workloads
(decision support analysis and interactive data-serving).
 Use YCSB and TPC-H DSS benchmarks respectively

4. BACKGROUND
 Parallel Data Warehouse (PDW)
 shared-nothing parallel database system built on top of SQL
Server
 multiple compute nodes, a single control node and other
administrative service nodes.
 Hive
 an open-source data warehouse built on top of Hadoop
 a structured data model for data that is stored in the Hadoop
Distributed Filesystem (HDFS), and a SQL-like declarative query
language called HiveQL

5. BACKGROUND(CONT.)
 MongoDB
Features
 a document-oriented storage layer, indexing in the form of B-
trees, auto-sharding, asynchronous replication of data between
servers.
 Data stored in collections which contain documents
 Each document is serialized using BSON
For implementation, it is created two types of MongoDB servers:
 MongoDB-CS (with client-side sharding )
 MongoDB-AS (Auto-Sharding)

6. EVALUATION
 Make hardware and software configuration for all four systems
 For PDW and Hive, use 8 disks to store the data
 For YCSB benchmark, 8 nodes are used as servers and another 8 for
client-benchmarks
Hive and Hadoop
 Use RCFile format to store data
 All TPC-H tables are stored in Gzip RcCile format

7. TRADITIONAL DSS WORKLOAD:
HIVE VS PDW
Workload Description
 use TPC-H at 4 scale factors (250,500,1000,4000,16000 GBs)
 TPC-H generator doesn’t produce correct result at 16000 scale
 Executed all 22 TPC-H queries
 But leave 2 TPC-H refresh functions

8. TRADITIONAL DSS WORKLOAD:
HIVE VS PDW
Data Layout in
Hive and PDW

9. TRADITIONAL DSS WORKLOAD:
HIVE VS PDW
Data Preparation and Load Times
Hive
 Generated dataset across 16 nodes
 Create one hive table for each TPC-H table
 Data is loaded in 2 phases:
 data files loaded onto each node
 data is converted from text to RCfile format.
PDW
 Load data into landed node
 Create necessary tables

10. TRADITIONAL DSS WORKLOAD:
HIVE VS PDW
Performance Analysis

11. TRADITIONAL DSS WORKLOAD:
HIVE VS PDW
Performance Analysis(cont.)
 PDW is faster than Hive in for all TPC-H queries
 The average speedup of PDW over Hive is greater for small datasets
 Hive has high overheads for small datasets.
Scalability Analysis
 Hive scales better than PDW
 Hive scales well as the dataset size increases.

12. MODERN OLTP WORKLOAD:
MONGODB VS SQL SERVER
Workload description
Extends YCSB into 2 ways:
 added support for multiple instances on many database servers
 Supports for Stored procedures in YCSB JBDC driver
ran the YCSB benchmark on a database that consists of 640 million records

13. MODERN OLTP WORKLOAD:
MONGODB VS SQL SERVER
Data Preparation
 Mongo-AS can automatically manage the shards by using a
“balancer” process
 The loading time for SQL-CS and Mongo-CS was 146 and 45
minutes respectively
 SQL load time take longer because a bulk insert method was not
used

14. MODERN OLTP WORKLOAD:
MONGODB VS SQL SERVER
Experimental Evaluation
“Read-Only” workload

15. MODERN OLTP WORKLOAD:
MONGODB VS SQL SERVER
95% Read
5% Update Workload

16. MODERN OLTP WORKLOAD:
MONGODB VS SQL SERVER
50% Read &
50% Update workload

17. MODERN OLTP WORKLOAD:
MONGODB VS SQL SERVER
95% Read
5% Append Workload

18. DISCUSSION & CONCLUSION
 This evaluation shows that NoSQL systems are still behind RDBMS in
performance.
 PDW is also 9 times faster than Hive running TPC-H at 16TB scale
 SQL-CS was able to achieve higher throughput than MongoDB

19. AUTHORS
 Avrilia Floratou
University of Wisconsin-Madison
 Nikhil Teletia
Microsoft Jim Gray Systems Lab
 David J. DeWitt
Microsoft Jim Gray Systems Lab
 Jignesh M. Patel
University of Wisconsin-Madison
 Donghui Zhang
Paradigm4