In memory databases presentation

In Memory Databases
An Overview
By John Sullivan
john@inmemory.net

Features
• Data is stored sequentially by Row
• Essentially an Array / List Structure
• Easy to Add / Update / Insert /Delete
• Need to read entire Row to get to
one Column’s Data

Features
• Data is stored by Column
• Faster to Read a few Columns
• Very Hard to Update / Insert
• Reading Data Sequentially from
Column, CPU Cache Friendly

Compressed Column Store
• Column Array is converted into 2 arrays
–One array contains a list of sorted
Unique Values
–Another array containing an integer
index to the values

Sqlite
• Opened by Special Filename :memory:
• Designed for Single Process / File
• Great for embedded systems/ mobile
devices. E.g. IOS Apps
• Row Store , No Column Store
• One Writer only. Non Server Based.
• Free & Open Source

Excel
• Power Pivot, Introduced in Excel 2010
• Non SQL Query Language
• Data Analysis Expressions (DAX)
• Syntax similar to Excel Formulae
• Requires Pro version of Office or Excel

Tableau
• Primarily a Visualization Tool
• Tableau Data Extracts (TDE)
• Compressed Column Store
• Generates one table flat Extract from Source (
that may involve joins )
• Uses ODBC / OLEDB For Extraction
• Only loads required columns from Extract

Qlik
• One of the Original Developers in Compressed
Columnar In Memory Analytics
• Nice Dashboards
• Incremental Updates
• Autojoins Fields based on Field Name
• Scripting Langauge for Generating QVD Files

Qlik Load Script Example
Companies:
LOAD id AS COMPANY_ID,
name as COMPANY_NAME,
postcode AS COMPANY_POSTCODE,
address AS COMPANY_ADDRESS,
If(id > 100, 1, 0) AS FLAG_NATIONAL;
SQL SELECT id, name, postcode, address
FROM database.Companies;

Monet DB
• Pioneer in Columnar Databases
• Research Focussed out of the Netherlands
• Open Source
• Can Cache Expensive Computations and Reuse
• Early versions was used by Data Distilleries,
which got bought out by SPSS
• R Integration

SQL Server Enterprise
• ColumnStore Indexes
–Data is stored by column.
–Blocks of 1,048,576 Values
• InMemory OLTP
(MEMORY_OPTIMIZED=ON) after Create Table
Data/Delta files of 128 MB

Oracle
• TimesTen
– Works with Oracle Database as a Cache
– Telecoms and Financial Companies
• Oracle 12 Enterprise
– Row & Column Formats
– In Memory Columnstore
• Exalytics

SAP Hana
• Pure In Memory Database
• In Memory OLTP Rowstore
• In Memory Columnstore
– Up to 2^31 rows per block
• Cluster Large Fact tables across nodes
• Hana One Available on EC2 & IBM

Memsql
• Pure In Memory Database
• Mysql Wire Protocol Compatible
• Lockfree Linked Lists and Skiplists
• SQL Queries compiled into C++
• Split Large Tables Across Nodes
• Column Store Aimed at Analytics
• Apache Spark Integration

Clustered Databases
• Amazon Redshift
• EMC Greenplum
• IBM Netezza
• HP Vertica
• Teradata

Other In Memory Players
• Sisense BI Focussed
• Parstream Cisco Owned
• Domo SAAS BI Company. Omniture Founder
• Iri
• InsightSquared BI Focussed
• VoltDB Java Stored Procedure Unit of Exec
• Infobright Open Sourced based on Mysql
• KDB Focussed on HFT / Terse

InMemory.Net
public static void testDoublePerformance() {
double total = 0;
for (int kk = 0; kk < 1000000000; kk++) {
total += kk;
}
Console.WriteLine(total);
}

Results
• Ran in about 2.5 second for a billion Rows
• 400 million rows per second on Single Core
• About 50% of performance of C++ Prog.
• 1.6 billion / second when running using 4 Core
• 2.0 billion / second when running with HT
Cores

Initial Version
• InMemoryColumn<T> {
Dictionary <T,int> initialValuesDict;
List <int> initialIndexes;
T [] finalValues;
int [] finalIndexes;
• }

Next Version
int [][] initialIndexes;
T [] finalValues;
int [] finalIndexes;
• }

Final Version
byte/ushort/int [][] initialIndexes;
T [] finalValues;
byte/ushort/int [] finalIndexes;
• }

Example Rule from Grammer
mainquery [ImpVars vars] returns [InMemoryQuery query ] :
{ $query = new InMemoryQuery(); }
SELECT1
(CACHE {$query.setCache();} )?
(NOCACHE {$query.setNoCache();} )?
(DISTINCT {$query.setDistinct();} )?
fieldclause [$query,$vars]
(
(INTO label { $query.setInto ($label.text2 ) ;})?
FROM tableclause [$query,$vars]
( (COMMA|CROSS JOIN ) tableclause [$query,$vars] ) *
(WHERE whereclause [$query,$vars])?
(GROUP BY groupclause [$query,$vars])?
(HAVING havingclause [$query,$vars])?
(ORDER BY orderclause [$query,$vars])?
(LIMIT limitclause [$query,$vars])?
)? ;

Code Generation
• Generate C# To Evaluate Query
• Compiled Code undergoes JIT for fast exec
• Parameterize Constants
– Simplify complex Constant Expressions
• Generic Table / Column Naming
• Reuse Generated Code

Detail Queries
• Detail Query
–Initial List Algorithm
–Improved by using Arrays of Arrays
–Only one thread works on one
Array

SELECT customerid FROM Orders
for (int tab1_counter = rowStart; tab1_counter < rowEnd; tab1_counter++,)
{ groupRowD1 = groupRowCount >> 14;
groupRowD2 = groupRowCount & 16383;
if (groupRowD2 == 0)
{
if (groupRowD1 > 0)
{
blockCounts[groupRowD1 - 1] = 16384;
}
lock (lock_newBlockObject)
{
groupRowCount = nextRecordD1 << 14;
nextRecordD1++;
}
groupRowD1 = groupRowCount >> 14;
t_total0[groupRowD1] = new byte[16384];
total0 = t_total0[groupRowD1];
};
total0[groupRowD2] = val_t1_c1[tab1_counter];
groupRowCount++;
if ((groupRowCount & 16383) == 0)
{
blockCounts[groupRowD1] = 16384;
}
}

Aggregative Queries
• Group Cardinality =1
• Group Cardinality < 500k
– Use Arrays of Arrays,
– Lookup Key being Group Index
• Group Cardinality > 500k
– Use Dictionaries to Correlate Group Index ->
Storage
– Arrays of Arrays

SELECT customer, SUM(1) FROM orders
WHERE employee=1 GROUP BY customer
for (int tab1_counter = rowStart; tab1_counter < rowEnd;
tab1_counter++, newRow = false) {
if ((val_t1_c2[tab1_counter] == const_0_t1_c2)) {
rowIndex = val_t1_c1[tab1_counter];
if (groupRowExists[rowIndex] == 0) newRow = true;
groupRowExists[rowIndex] = 1;
total1[rowIndex] += const_0;
if (newRow) {
total0[rowIndex]=val_t1_c1[tab1_counter];
}
}
}

COUNT DISTINCT
• Initial Algorithm used Byte []
• Used lots of Memory on Large Cores
• Upgraded to 1 [] across all Cores
• Interlocked.CompareExchange to set Bit
• Hashmap for initial Values
• Then switch to byte []

Subqueries
• Subquery in Table clause can be materialized
into temp table ( CACHE )
• Simplify Subquery ( NOCACHE)
Only Fields Parent SELECT Requires
Pass Through Parent WHERE Clause

JOINS
• LEFT & INNER JOIN SUPPORT
• Merge Parent & Child Column Values
• Parent Value -> Child Indexes
• ONE to ONE
– Join becomes an Array Lookup
• ONE to Many
– Join Becomes for Loop

Query Simplification
• Rewrite Aggregate Queries with Expressions
SELECT SUM(1) / SUM (qty ) FROM Orders
SELECT SUM(1) as A, SUM(QTY) as B from
Orders
SELECT A/B FROM TEMP_QUERY

More Simplifications
• Group Expressions with 1 Database Field
e.g. Group by Month ( OrderDate )
Inner Join OrderDate to Table of Its Unique
Values and Month ( OrderDate )
• Remove Redundant Group By Parts
Group BY OrderDate , Month ( Orderdate )
Group BY OrderDate , Month ( Orderdate )

HAVING Clause
• Convert to two Queries
• One Query without Having Clause
• Having Clause becomes Where of Second
Query

Special Cases
• SELECT DISCOUNT ( COUNT CUSTOMER )
FROM ORDERS
• Answer is No of Customer Values
• SELECT DISTINCT CUSTOMER FROM ORDERS
Answer is List of Customer Unique Values

Importing Data
DATASOURCE a1=ODBC 'dsn=ir_northwind'
IMPORT Customers=a1.customers
IMPORT Products=a1.{SELECT * FROM Products}
IMPORT orders-a1.'somequery.sql'
SAVE

Importing Data II
• ODBC / OLEDB / DOT NET Providers
• Special ME Datasource
• Existing In Memory Databases
• UNION ALL Between Sources
• SLURP Command
• Variables, Expressions & IF

Interfacing to the Database
• Native Dot Net API
• Dot Net Data Provider
• COM/ ACTIVEX API
• ODBC Driver
C / C++ IO
Licensed ODBC Kit
Parameterized Queries + Cursor Support

Hard Learned Lessons
• Allocated and Store Variables Relating to One
Thread Sequentially. Don’t intermix
• Xeon Servers with Maxed out memory can
have slower memory access speed
– 1 Rank 1,866 Mhz
– 2 Ranks 1,600 Mhz
– 3 Ranks 1,333 Mhz

Bitcoin Mining / HFT
• CPUS
• GPUs
• FPGAs
• Dedicated Mining Chip

GPU & InMemory Databases
• GPUDB, MAPD
– Good for Visualising Billions of Points
– GPUs can run thousands of Cores on Data
– GPU to Main Memory Bottleneck
– Potentially more Data Reduction
• Blazegraph, Graphsql
Fast Graph Database that can use GPU

FPGA Potential
• Field-Programmable Gate Array
– is an integrated circuit designed to be configured
by a customer or a designer after manufacturing
– Programmable Integrated Circuit
• Could be used to enhanced In Memory DBs
• Intel bought Altera back in June 2015
– Will roll technology out into Data Center

Hardware Transaction Memory
• Simplifies Concurrent Programming
– Group of Load & Store Instructions
– Can Execute Atomically
• Hardware of Software Transactional Memory
• Intel TSX
– Transaction Synchronization Extensions
– Available in some Skylake Processors
– Added to Haswell/Broadwell but Disabled

3D XPoint Memory
• Announced by Intel & Micron June 2015
• 1000 times more Durable than Flash
• Like DRAM that has Permanence
• Latency 10 times faster than NAND SSD
• 4-6 Times slower than DRAM

Thanks for help with Market Research
• Dan Khasis
• Niall Dalton
• Jeff Cordova – Wavefront
• SapHanaTutorial.com

In memory databases presentation

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