The document describes an in-memory OLAP engine created by Samuel Pelletier to enable fast querying of multidimensional data with millions of facts. It loads data into memory as plain old Java objects (POJOs) for faster access compared to entity objects or SQL queries. Dimensions are modeled as classes to index the facts and compute summarized results. The engine is multithreaded and designed for simplicity and minimal dependencies.

1. In memory OLAP engine
Samuel Pelletier
Kaviju inc.
samuel@kaviju.com

2. OLAP ?
• An acronym for OnLine Analytical Processing.
• In simple words, a system to query a multidimensional data set
and get answer fast for interactive reports.
• A well known implementation is an Excel Pivot Table.

3. Why build something new
• I wanted something fast, memory efficient for simple queries with
millions of facts.
• Sql queries dost not works for millions of facts with multiple
dimensions, especially with large number of rows.
• There are specialized tools for OLAP from Microsoft, Oracle and
others but they are large and expensive, too much for my needs.
• Generic cheap toolkits are not memory efficient, this is the cost for
their simplicity.
• I wanted a simple solution to deploy with minimal dependency.

4. Memory usage and time to
retrieve 1 000 000 invoice lines
• Fetching EOs uses 1.2 GB of ram in 13-19 s
• Fetching raw rows uses 750 MB of ram in 5-8 s.
• Fetching as POJOs with jdbc uses 130 MB in 4.0 s.
• Reading from file as POJOs uses 130 MB in 1.4 s.
• For 7 M rows, EOs would require 8.4 GB for gazillions of small
objects (bad for the GC).

5. Time to compute sum of sales for
1 000 000 invoice lines
• 2.1 s for "select sum(sales)..." in FrontBase with table in RAM.
• 0.5 s for @sum.sales on EOs.
• 0.12 s for @sum.sales on raw rows.
• 0.5 s for @sum.sales on POJOs.
• 0.009 s for a loop with direct attribute access on POJOs.

6. Some concepts
• Facts are the elements being analyzed.An exemple is invoice
lines.
• Facts contains measures like quantities, prices or amounts.
• Facts are linked to dimensions used to filter and aggregate
them. For invoice lines, we have product, invoice, date, etc.
• Dimensions are often part of a hierarchy, for example, products
are in a product category, dates are in a month and in a week.

7. Sample Invoice dimension hierarchy
Invoice
Line
Invoice
Date
Month
Ship to Client type
Sold to
Product
Salesman
SalesManager
Week
Client type
Measures:
Shipped Qty
Sales
Profits

8. Steps to implement an engine
• Create the Engine class.
• Create required classes to model the dimension hierarchy.
• Create theValue class for your facts.
• Create the Group class that will compute summarized results.
• Create the dimensions definition classes.

9. Engine class
• Engine class extends OlapEngine with Group andValue types.
public class SalesEngine extends OlapEngine<GroupEntry,Value>
• Create the objects required for the data model and lookup table
used to load the facts.
• Load the fact intoValue objects.
• Create and register the dimensions.

10. Create required model objects
public class Product {
public final int code;
public final String name;
public final ProductCategory category;
public Product(int code, String name, ProductCategory category) {
super();
this.code = code;
this.name = name;
this.category = category;
}
}
!
private void loadProducts() {
productsByCode = new HashMap<Integer, Product>();
!
WOResourceManager resourceManager = ERXApplication.application().resourceManager();
String fileName = "olapData/products.txt";
try ( InputStream fileData = resourceManager.inputStreamForResourceNamed(fileName, null, null);) {
InputStreamReader fileReader = new InputStreamReader(fileData, "utf-8");
BufferedReader reader = new BufferedReader(fileReader);
String line;
while ( (line = reader.readLine()) != null) {
String[] cols = line.split("t", -1);
Product product = new Product(Integer.parseInt(cols[0]), cols[0], categoryWithID(cols[1]));
productsByCode.put(product.code, product);
}
}
...
}

11. Load the facts and create dimensions
private void loadInvoiceLines() {
...
loadProductCategories();
loadProducts();
!
InvoiceDimension invoiceDim = new InvoiceDimension(this);
SalesmanDimension salesmanDim = new SalesmanDimension(this);
while ( (line = reader.readLine()) != null) {
String[] cols = line.split("t", -1);
!
InvoiceLine invoiceLine = new InvoiceLine(valueIndex++, Short.parseShort(cols[1]));
invoiceLine.shippedQty = Integer.parseInt(cols[6]);
invoiceLine.sales = Float.parseFloat(cols[7]);
invoiceLine.profits = Float.parseFloat(cols[8]);
lines.add(invoiceLine);
invoiceDim.addLine(invoiceLine, cols[0], cols);
!
invoiceLine.salesmanNumber = Integer.parseInt(cols[12]);
salesmanDim.addIndexEntry(invoiceLine.salesmanNumber, invoiceLine);
...
}
}
addDimension(productDimension);
addDimension(productDimension.createProductCategoryDimension());
...
lines.trimToSize();
setValues(lines);
}

12. Value and GroupEntry classes
• Value classe contains your basic facts (invoice lines for example)
public class InvoiceLine extends OlapValue<Sales>
• GroupEntry is use to compute summarized results.
public class Sales extends GroupEntry<InvoiceLine>
• These are tightly coupled, a GroupEntry represent a computed
result for an array ofValues; metrics are found in both classes.

13. Value Class
public class InvoiceLine extends OlapValue<Sales> {
public Invoice invoice;
public final short lineNumber;
public Product product;
!
public int shippedQty;
public float sales;
public float profits;
!
public int salesmanNumber;
public int salesManagerNumber;
!
public InvoiceLine(int valueIndex, short lineNumber) {
super(valueIndex);
this.lineNumber = lineNumber;
}
}

14. GroupEntry class
public class Sales extends GroupEntry<InvoiceLine> {
private int shippedQty;
private double sales = 0.0;
private double profits = 0.0;
!
public Sales(GroupEntryKey<Sales, InvoiceLine> key) {
super(key);
}
!
@Override
public void addEntry(InvoiceLine entry) {
shippedQty += entry.shippedQty;
sales += entry.sales;
profits += entry.profits;
}
!
@Override
public void optimizeMemoryUsage() {
}
return sales;
}
!
...
}

15. Dimensions classes
• Dimensions implement the engine indexes and key extraction for
result aggregation.
• Dimensions are usually linked to another class representing an
entity like Invoice, Client, Product or ProductCatogory.
• Entity are value object POJO for optimal speed an memory
usage.You may add a method to get the corresponding eo.
• Dimensions are either leaf (a group of facts) or group (a group of
leaf entries).

16. Product dimension class
public class ProductDimension extends OlapLeafDimension<Sales,Integer,InvoiceLine> {
!
public ProductDimension(OlapEngine<Sales, InvoiceLine> engine) {
super(engine, "productCode");
}
!
@Override
public Integer getKeyForEntry(InvoiceLine entry) {
return entry.product.code;
}
!
@Override
public Integer getKeyForString(String keyString) {
return Integer.valueOf(keyString);
}
public ProductCategoryDimension createProductCategoryDimension() {
long startTime = System.currentTimeMillis();
ProductCategoryDimension dimension = new ProductCategoryDimension(engine, this);
!
for (Product product : salesEngine().products()) {
dimension.addIndexEntry(product.category.categoryID, product.code);
}
long fetchTime = System.currentTimeMillis() - startTime;
engine.logMessage("createProductCategoryDimension completed in "+fetchTime+"ms.");
return dimension;
}
!
private SalesEngine salesEngine() {
return (SalesEngine) engine;
}

17. Product category dimension class
public class ProductCategoryDimension extends OlapGroupDimension<Sales,Integer,InvoiceLine,ProductDimension,Integer> {
!
public ProductCategoryDimension(OlapEngine<Sales, InvoiceLine> engine, ProductDimension childDimension) {
super(engine, "productCategoryCode", childDimension);
}
!
@Override
public Integer getKeyForEntry(InvoiceLine entry) {
return entry.product.category.categoryID;
}
!
@Override
public Integer getKeyForString(String keyString) {
return Integer.valueOf(keyString);
}

18. Initialize and use in an app
• The engine is multithread capable once loaded.
• I usually create a singleton for the engine; it can also be in your
app class.
• Entity are value object POJO for optimal speed an memory
usage.You may add a method to get the corresponding eo.
• Dimensions are either leaf (a group of facts) or group (a group of
leaf entries).

19. Use in a application
public Application() {
...
SalesEngine.createEngine();
}
!
!
In the component that uses the engine
!
public OlapNavigator(WOContext context) {
super(context);
....
engine = SalesEngine.sharedEngine();
if (engine == null) {
Engine me bay null if it has not completed it's loading...
}
}
!
someFetchMethod() {
OlapResult<Sales, InvoiceLine> result = engine.resultForRequest(query);
!
rows = new NSArray<Sales>(result.getGroups());
sort or put inside a ERXDisplayGroup...
}
!

20. Demo app

21. Java and memory
• To keep the garbage collector happy, it is better to have a
maximum heap at least 2-3 times the real usage.
• GC can kill your app performance if memory is starved.With
default setting, it may even kill your server by using multiple core
for long periods at least in 1.5 and 1.6.
• Java 1.7 contains a new collector, probable better.

22. Q&A
Samuel Pelletier
samuel@kaviju.com