The thinking persons guide to data warehouse design

The Thinking Person’s Guide to Data Warehouse Design Robin Schumacher VP Products Calpont

Agenda Building a logical design Transitioning to a physical design Monitoring and tuning the design

Building a logical design

Why care about design…?

What is the key component for success? In other words, what you do with your MySQL Server – in terms of physical design, schema design, and performance design – will be the biggest factor on whether a BI system hits the mark… * Philip Russom, “Next Generation Data Warehouse Platforms”, TDWI, 2009. *

First – get/use a modeling tool

The logical design for OLTP

Simple reporting databases OLTP Database Read Shard One Reporting Database Application Servers End Users ETL Just use the same design on a different box… Replication

Horror story number one…

The logical design for analytics/data warehousing

Logical Design Considerations
Datatypes are more generally defined, not directed toward a database engine
Entities aren’t designed for performance necessarily
Redundancy is avoided, but simplicity is still a goal
Bottom line: you want to make sure your data is correctly represented and is easily understood (new class of user today)

Manual horizontal partitioning Modeling technique to overcome large data volumes

Manual Vertical Partitioning Modeling technique to overcome wide tables/rows

Pro’s/con’s to manual partitioning
More tables to manage
More referential integrity to manage
More indexes to manage
Joins oftentimes needed to accomplish query requests
Oftentimes, a redesign is needed because the rows / columns you thought you’d be accessing together change; it’s hard to predict ad-hoc query traffic
Less I/O if design holds up
Easy to prune obsolete data
Possibly less object contention
Pro’s Con’s

The bottom line on logical modeling
Use a modeling tool to capture your designs
Do not utilize a third-normal form design for analytics; keep it simple and understandable
Manual partitioning is OK in some cases, but,..
Let the database engine do the work for you

Transitioning to a physical design

SQL or NoSQL…? Row or Column database…? How to scale…? Should I worry about High availability…? Index or no…? How should I partition my data…? Is sharding a good idea…?

General list of top BI database design decisions
General architecture / data orientation
Storage engine selection
Physical table/Index partitioning
Indexing creation and placement
Optimizing data loads

Divide & conquer is the best approach
Whether you choose to go NoSQL, Shard with normal or special MySQL engines, use MPP storage engines, or something similar, divide & conquer is your best friend
You can scale-up and divide & conquer to a point, but you will hit disk, memory, or other limitations
Scaling up and out is the best future proof methodology

Divide & conquer via sharding

What technologies you should be looking at * Philip Russom, “Next Generation Data Warehouse Platforms”, TDWI, 2009. *

Row or column-based engine? Medium-very large data Small-medium data Very dynamic; query patterns change Know exactly what to index; won’t change Need very fast loads; little DML Will be doing lots of single inserts/deletes Only need subset of columns for query Will need most columns in a table for query Yes, Column-based tables! Yes, Row-based tables!

Column vs. row orientation A column-oriented architecture looks the same on the surface, but stores data differently than legacy/row-based databases…

Example: InfiniDB vs. “Leading” row DB InfiniDB takes up 22% less space InfiniDB loaded data 22% faster InfiniDB total query times were 65% less InfiniDB average query times were 59% less Notice not only are the queries faster, but also more predictable * Tests run on standalone machine: 16 CPU, 16GB RAM, CentOS 5.4 with 2TB of raw data

Why not use both…?
You can create a hybrid system where you use row-based tables and column-based tables in the same instance and same database
Use InnoDB for OLTP or MyISAM for certain read operations
Use column-based tables for analytics, data marts, or warehouses
You can scale out with column tables and use row-based tables locally

Why not use both…?

Most used DW Storage engines internal to MySQL MyISAM Archive Memory CSV
High-speed query/insert engine
Non-transactional, table locking
Good for data marts, small warehouses
Compresses data by up to 80%
Fastest for data loads
Only allows inserts/selects
Good for seldom accessed data
Main memory tables
Good for small dimension tables
B-tree and hash indexes
Comma separated values
Allows both flat file access and editing as well as SQL query/DML
Allows instantaneous data loads
Also:Merge for pre-5.1 partitioning

What about NoSQL options?
Standard model is not relational
Typically don’t use SQL to access the data
Take up more space than column databases
Lack special optimizers / features to reduce I/O
Really are row-oriented architectures that store data in ‘column families, which are expected to be accessed together (remember logical vertical partitioning?) Individual columns cannot be accessed independently
Will be faster with individual insert and delete operations
Will normally be faster with single row requests
Will lag in typical analytic / data warehouse use cases

Partitioning – not ‘if’ but ‘how’
mysql> CREATE TABLE part_tab
-> ( c1 int ,c2 varchar(30) ,c3 date )
-> PARTITION BY RANGE (year(c3)) (PARTITION p0 VALUES LESS THAN (1995),
-> PARTITION p1 VALUES LESS THAN (1996) , PARTITION p2 VALUES LESS THAN (1997) ,
-> PARTITION p9 VALUES LESS THAN (2004) , PARTITION p10 VALUES LESS THAN (2010),
-> PARTITION p11 VALUES LESS THAN MAXVALUE );
mysql> create table no_part_tab (c1 int,c2 varchar(30), c3 date);
*** Load 8 million rows of data into each table ***
mysql> select count(*) from no_part_tab where c3 > date '1995-01-01' and c3 < date '1995-12-31';
+----------+
| count(*) |
+----------+
| 795181 |
+----------+
1 row in set (38.30 sec)
mysql> select count(*) from part_tab where c3 > date '1995-01-01' and c3 < date '1995-12-31';
+----------+
| count(*) |
+----------+
| 795181 |
+----------+
1 row in set (3.88 sec)
90% Response Time Reduction

Partitioning – Stripe your Partitions
CREATE TABLE T1 (col1 INT, col2 CHAR(5), col3 DATE) ENGINE=MYISAM
PARTITION BY HASH(col1)
(
PARTITION P1
DATA DIRECTORY = '/appdata1/data',
PARTITION P2
DATA DIRECTORY = '/appdata2/data',
PARTITION P3
DATA DIRECTORY = '/appdata3/data’,
PARTITION P4
DATA DIRECTORY = '/appdata4/data’
);
Note that striping only works for some engines (e.g. MyISAM, Archive) and for only certain operating systems (e.g. the option is ignored on Windows). You can use the REORGANIZE PARTITION command to move current partitions to new devices.

Partitioning – Smart Data Pruning
mysql> delete from t2 where
-> c3 > date '1995-01-01' and c3 < date '1995-12-31';
Query OK, 805114 rows affected (47.41 sec)
Most data warehouses have pruning or obsolete data operations that remove unwanted data. Using partitioning allows you to much more quickly and efficiently remove obsolete data: mysql> alter table t1 drop partition p1; Query OK, 0 rows affected (0.03 sec) Records: 0 Duplicates: 0 Warnings: 0 VS. The DROP PARTITION is A DDL operation, which runs much faster than a DML DELETE.

Index Creation and Placement
If query patterns are known and predictable, and data is relatively static, then indexing isn’t that difficult
If the situation is a very ad-hoc environment, indexing becomes more difficult. Must analyze SQL traffic and index the best you can
Over-indexing a table that is frequently loaded / refreshed / updated can severely impact load and DML performance. Test dropping and re-creating indexes vs. doing in-place loads and DML. Realize, though, any queries will be impacted from dropped indexes
Index maintenance (rebuilds, etc.) can cause issues in MySQL (locking, etc.)
Remember some storage engines don’t support normal indexes (Archive, CSV)
Remember that a benefit of (most) column databases is that they do not need or use indexes

Optimizing for data loads
The two biggest killers of load performance are (1) very wide tables for row-based tables; (2) many indexes on a table;
Stating the obvious, LOAD DATA INFILE and the high-speed loaders of column-based engines are the fastest way to load data vs. singleton or array insert statements
Column-based tables typically load faster than row-based tables with load utilities, however they will experience slower insert/delete rates than row-based tables
Loading data in primary key format helps some engines (e.g. InnoDB).

Optimizing for data loads
Move the data as close to the database as possible; avoid having applications on remote machines do data manipulations and send data across the wire a row at a time – perhaps the worst way to load data
Oftentimes good to create staging tables then use procedural language to do data modifications and/or create flat files for high speed loaders
Loading data via time-based order helps some column databases like InfiniDB; logical range partitioning is then possible

Monitoring and tuning the design

Three performance analysis methods Bottleneck analysis Workload analysis Ratio analysis

Bottleneck analysis
The focus of this methodology is the answer to the question “what am I waiting on?”
With MySQL, unfortunately, it can be difficult to determine latency in the database server
Lock contention rarely an issue in data warehouses
New MySQL performance schema has a ways to go in my opinion to be truly useful for bottleneck analysis
Problems found in bottleneck analysis translate into better lock handling in the app, partitioning improvements, better indexing, or storage engine replacement

Workload analysis
The focus of this methodology is the answer to three questions: (1) Who’s logged on?; (2) What are they doing?; (3) How is my machine handing it?
Monitor active and inactive sessions. Keep in mind idle connections do take up resources
I/O and ‘hot objects’ a key area of analysis
Key focus should be on SQL statement monitoring and collection; something that goes beyond standard pre-production EXPLAIN analysis

Horror story number two…

The pain of slow SQL * Philip Russom, “Next Generation Data Warehouse Platforms”, TDWI, 2009.

Workload analysis
SQL analysis basically becomes bottleneck analysis, because you’re asking where your SQL statement is spending its time
Once you have collected and identified your ‘top SQL’, the next step is to do tracing and interrogation into each SQL statement to understand its execution
Historical analysis is important too; a query that ran fine with 5 million rows may tank with 50 million or with more concurrent users
Design changes usually involve data file striping, indexing, partitioning, or parallel processing additions

Ratio analysis
Least useful of all the performance analysis methods
May be OK to get a general rule of thumb as to how various resources are being used
Do not be misled by ratios; for example, a high cache hit ratio is sometimes meaningless. Databases can be brought to their knees by excessive logical I/O
Design changes from ratios typically include the altering of configuration parameters and sometimes indexing

Conclusions
Design is the #1 contributor to the overall performance and availability of a system
With MySQL, you have greater flexibility and opportunity than ever before to build well-designed data warehouses
With MySQL, you now have more options and features available than ever before
The above translates into you being able to design data warehouses that can be future proofed: they can run as fast as you’d like (hopefully) and store as much data as you need (ditto)

For More Information
Download InfiniDB Community Edition
Download InfiniDB documentation
Read InfiniDB technical white papers
Read InfiniDB intro articles on MySQL dev zone
Visit InfiniDB online forums
Trial the InfiniDB Enterprise Edition: http://www.calpont.com
www.infinidb.org www.calpont.com

The Thinking Person’s Guide to Data Warehouse Design Robin Schumacher [email_address]

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The thinking persons guide to data warehouse design

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