This article documents how to solve an event processing problem in a highly efficient manner through the reduction of waste in the software stack.

Java is often seen as a memory hog that cannot operate efficiently in low memory environments. My aim is to demonstrate what many think is impossible, that a meaningful java program can operate in almost no memory. The example processes 2.2 million CSV records per second in a 3MB heap with zero GC on a single thread in Java.

You will learn where the main areas of waste exist in a Java application and the patterns that can be employed to reduce them. The concept of zero cost abstraction is introduced, and that many optimizations can be automated at compile time through code generation. A maven plugin simplifies the developer workflow.

Our goal is not high performance, that comes as a by-product of maximizing efficiency. The solution employs Fluxtion which uses a fraction of the resources compared with existing java event processing frameworks.

Computing and the Climate

Climate change and its causes are currently of great concern to many. Computing is a major source of emissions, producing the same carbon footprint as the entire airline industry. In the absence of regulation dictating computing energy consumption we, as engineers, have to assume the responsibility for producing efficient systems balanced against the cost to create them.

On a panel session from InfoQ 2019 in London, Martin Thompson spoke passionately about building energy-efficient computing systems. He noted controlling waste is the critical factor in minimizing energy consumption. Martin's comments resonated with me, as the core philosophy behind Fluxtion is to remove unnecessary resource consumption. That panel session was the inspiration for this article.

Processing Requirements

Requirements for the processing example are:

Operate in 3MB of the heap with zero GC

Use standard Java libraries only, no "unsafe" optimizations

Read a CSV file containing millions of rows of input data

Input is a set of unknown events, no pre-loading of data

Data rows are heterogeneous types

Process each row to calculate multiple aggregate values

Calculations are conditional on the row type and data content

Apply rules to aggregates and count rule breaches

Data is randomly distributed to prevent branch prediction

Partition calculations based on row input values

Collect and group partitioned calculations into an aggregate view

Publish a summary report at the end of file

Pure Java solution using high-level functions

No JIT warm-up

Example Position and Profit Monitoring

The CSV file contains trades and prices for a range of assets, one record per row. Position and profit calculations for each asset are partitioned in their own memory space. Asset calculations are updated on every matching input event. Profits for all assets will be aggregated into a portfolio profit. Each asset monitors its current position/profit state and records a count if either breaches a pre-set limit. The profit of the portfolio will be monitored and loss breaches counted.

Rules are validated at the asset and portfolio level for each incoming event. Counts of rule breaches are updated as events are streamed into the system.

Row Data Types

AssetPrice - [price: double] [symbol: CharSequence] Deal - [price: double] [symbol: CharSequence] [size: int]





Sample Data

The CSV file has a header lines for each type to allow dynamic column position to field mapping. Each row is preceded with the simple class name of the target type to marshal into. A sample set of records including header: Deal,symbol,size,price AssetPrice,symbol,price AssetPrice,FORD,15.0284 AssetPrice,APPL,16.4255 Deal,AMZN,-2000,15.9354

Calculation Description

Asset calculations are partitioned by a symbol and then gathered into a portfolio calculation.

Partitioned Asset Calculations

asset position = sum(Deal::size) deal cash value = (Deal::price) X (Deal::size) X -1 cash position = sum(deal cash value) mark to market = (asset position) X (AssetPrice::price) profit = (asset mark to market) + (cash position)





Portfolio Calculations

portfolio profit = sum(asset profit)





Monitoring Rules

asset loss > 2,000 asset position outside of range +- 200 portfolio loss > 10,000





NOTE:

A count is made when a notifier indicates a rule breach. The notifier only fires on the first breach until it is reset. The notifier is reset when the rule becomes valid again. A positive deal::size is a buy, a negative value a sell.

Execution Environment

To ensure memory requirements are met (zero GC and 3MB heap), the Epsilon no-op garbage collector is used, with a max heap size of 3MB. If more than 3MB of memory is allocated throughout the life of the process, the JVM will immediately exit with an out of memory error.

To run the sample: clone from git, and in the root of the trading-monitor project, run the jar file in the dist directory to generate a test data file of 4 million rows.

git clone --branch article_may2019 https://github.com/gregv12/articles.git cd articles/2019/may/trading-monitor/ jdk-12.0.1\bin\java.exe -jar dist\tradingmonitor.jar 4000000





Input Data

By default. the tradingmonitor.jar processes the data/generated-data.csv file. Using the command above, the input data should have the following characteristics:

Input file = data/generated-data.csv Trade count = 200,000 price updates = 3,800,000 Asset count = 7 File size = 96 MB





A utility is provided to generate input data files of different data sizes, see GenerateTestData.java.

Results

To execute the test, run the tradingmonitor.jar with no arguments:

jdk-12.0.1\bin\java.exe -verbose:gc -Xmx3M -XX:+UnlockExperimentalVMOptions -XX:+UseEpsilonGC -jar dist\tradingmonitor.jar





Executing the test for 4 million rows the summary results are:

Process row count = 4 million Processing time = 1.815 seconds Avg row exec time = 453 nano seconds Process rate = 2.205 million records per second garbage collections = 0 allocated mem total = 2857 KB allocated mem per run = 90 KB OS = windows 10 Processor = Intel core i7-7700@3.6Ghz Memory = 16 GB Disk = 512GB Samsung SSD PM961 NVMe





NOTE: Results are from the first run without JIT warm-up. After JIT warm-up, the code execution times are approx 10 percent quicker. Total allocated memory is 2.86MB, which includes starting the JVM.

Analysing Epsilon's output, we estimate the app allocates 15 percent of memory for 6 runs, or 90KB per run. There is a good chance the application data will fit inside L1 cache, but more investigations are required here.

Output

The test program loops 6 times. Printing out the results each time, Epsilon records memory statistics at the end of the run.

jdk-11.0.1\bin\java.exe" -server -XX:+UnlockExperimentalVMOptions -XX:+UseEpsilonGC -Xmx3M -verbose:gc -jar dist\tradingmonitor.jar [0.014s][info][gc] Non-resizeable heap; start/max: 3M [0.015s][info][gc] Using TLAB allocation; max: 4096K [0.016s][info][gc] Elastic TLABs enabled; elasticity: 1.10x [0.018s][info][gc] Elastic TLABs decay enabled; decay time: 1000ms [0.019s][info][gc] Using Epsilon [0.057s][info][gc] Heap: 3M reserved, 3M (100.00%) committed, 0M (5.45%) used ..... [0.263s][info][gc] Heap: 3M reserved, 3M (100.00%) committed, 2M (73.88%) used portfolio loss gt 10k count -> 1894405.0 Portfolio PnL:-1919.9455999995407 Assett positions: ----------------------------- MSFT : AssetTradePos{symbol=MSFT, pnl=-388.0564999999906, assetPos=420.0, mtm=7323.246, cashPos=-7711.302499999991, positionBreaches=156, pnlBreaches=51015} GOOG : AssetTradePos{symbol=GOOG, pnl=-359.2007000000076, assetPos=319.0, mtm=5135.4215, cashPos=-5494.622200000008, positionBreaches=335, pnlBreaches=104282} APPL : AssetTradePos{symbol=APPL, pnl=14.577800000011848, assetPos=-600.0, mtm=-9936.42, cashPos=9950.997800000012, positionBreaches=46, pnlBreaches=69790} ORCL : AssetTradePos{symbol=ORCL, pnl=72.58320000000003, assetPos=-404.0, mtm=-6327.0036, cashPos=6399.5868, positionBreaches=66, pnlBreaches=40570} FORD : AssetTradePos{symbol=FORD, pnl=-1339.9584999997278, assetPos=1359.0, mtm=21383.457300000002, cashPos=-22723.41579999973, positionBreaches=5, pnlBreaches=116290} BTMN : AssetTradePos{symbol=BTMN, pnl=579.833700000183, assetPos=-513.0, mtm=-8037.8379, cashPos=8617.671600000183, positionBreaches=29, pnlBreaches=106779} AMZN : AssetTradePos{symbol=AMZN, pnl=-499.72460000000956, assetPos=79.0, mtm=1249.1322, cashPos=-1748.8568000000096, positionBreaches=68, pnlBreaches=27527} millis:1814 ... ... starting portfolio loss gt 10k count -> 1894405.0 Portfolio PnL:-1919.9455999995407 Assett positions: ----------------------------- MSFT : AssetTradePos{symbol=MSFT, pnl=-388.0564999999906, assetPos=420.0, mtm=7323.246, cashPos=-7711.302499999991, positionBreaches=156, pnlBreaches=51015} GOOG : AssetTradePos{symbol=GOOG, pnl=-359.2007000000076, assetPos=319.0, mtm=5135.4215, cashPos=-5494.622200000008, positionBreaches=335, pnlBreaches=104282} APPL : AssetTradePos{symbol=APPL, pnl=14.577800000011848, assetPos=-600.0, mtm=-9936.42, cashPos=9950.997800000012, positionBreaches=46, pnlBreaches=69790} ORCL : AssetTradePos{symbol=ORCL, pnl=72.58320000000003, assetPos=-404.0, mtm=-6327.0036, cashPos=6399.5868, positionBreaches=66, pnlBreaches=40570} FORD : AssetTradePos{symbol=FORD, pnl=-1339.9584999997278, assetPos=1359.0, mtm=21383.457300000002, cashPos=-22723.41579999973, positionBreaches=5, pnlBreaches=116290} BTMN : AssetTradePos{symbol=BTMN, pnl=579.833700000183, assetPos=-513.0, mtm=-8037.8379, cashPos=8617.671600000183, positionBreaches=29, pnlBreaches=106779} AMZN : AssetTradePos{symbol=AMZN, pnl=-499.72460000000956, assetPos=79.0, mtm=1249.1322, cashPos=-1748.8568000000096, positionBreaches=68, pnlBreaches=27527} millis:1513 [14.870s][info][gc] Total allocated: 2830 KB [14.871s][info][gc] Average allocation rate: 19030 KB/sec





Waste Hotspots

The table below identifies functions in the processing loop that traditionally create waste and waste avoidance techniques utilized in the example.

Function Source of waste Effect Avoidance Read CSV file Allocate a new String for each row GC Read each byte into a flyweight and process in allocation free decoder Data holder for row Allocate a data instance for each row GC Flyweight single data instance Read col values Allocate an array of Strings for each column GC Push chars into a re-usable char buffer Convert value to type String to type conversions allocate memory GC Zero allocation converters CharSequence in place of Strings Push col value to holder Autoboxing for primitive types allocates memory. GC Primitive aware functions push data. Zero allocation Partitioning data processing Data partitions process in parallel. Tasks allocated to queues GC / Lock Single thread processing, no allocation or locks Calculations Autoboxing, immutable types allocating intermediate instances. State free functions require external state storage and allocation GC Generate functions with no autoboxing. Stateful functions zero allocation Gathering summary calc Push results from partition threads onto queue. Requires allocation and synchronization GC / Lock Single thread processing, no allocation or locks

Waste Reduction Solutions

The code that implements the event processing is generated using Fluxtion. Generating a solution allows for a zero-cost abstraction approach where the compiled solution has minimum overhead. The programmer describes the desired behavior, and at build time, an optimized solution is generated that meets the requirements. For this example, the generated code can be viewed here.

The Maven POM contains a profile for rebuilding the generated files using the Fluxtion Maven plugin executed with the following command:

mvn -Pfluxtion install





File Reading

CharEvent . As the same CharEvent instance is re-used, no memory is allocated after initialization. The logic for streaming CharEvent s is located in the Data is extracted from the input file as a series of CharEvents and published to the CSV-type marshaller. Each character is individually read from the file and pushed into a. As the sameinstance is re-used, no memory is allocated after initialization. The logic for streamings is located in the CharStreamer class. The whole 96 MB file can be read with almost zero memory allocated on the heap by the application.

CSV Processing

Adding a @CsvMarshaller to a javabean notifies Fluxtion to generate a CSV parser at build time. Fluxtion scans application classes for the @CsvMarshaller annotation and generates marshallers as part of the build process. For example, see AssetPrice.java, which results in the generation of AssetPriceCsvDecoder0. The decoder processes CharEvent s and marshalls the row data into a target instance.

The generated CSV parsers employ the strategies outlined in the table above, avoiding any unnecessary memory allocation and re-using object instances for each row processed:

A single re-usable instance of a characters buffer stores the row characters

A flyweight re-usable instance is the target for the marshaled row data

Conversions are performed directly from a CharSequence into target types without intermediate object creation.

If CharSequence s are used in the target instance field, then no strings are created; a flyweight Charsequence is used.

For example, a waste-free char targets field conversion and sees the upateTarget() method in a AssetPriceCsvDecoder0 ; this is generated by Fluxtion:

private boolean updateTarget() { try { updateFieldIndex(); fieldIndex = fieldName_price; setPrice.subSequence(delimIndex[fieldName_price], delimIndex[fieldName_price + 1] - 1); target.setPrice(atod(setPrice)); fieldIndex = fieldName_symbol; setSymbol.subSequence(delimIndex[fieldName_symbol], delimIndex[fieldName_symbol + 1] - 1); target.setSymbol(setSymbol); } catch (Exception e) { .... } return true; }





Calculations

This builder describes the asset calculation using the Fluxtion streaming API. The declarative form is similar to the Java stream API but builds real-time event processing graphs. Methods marked with the annotation @SepBuilder are invoked by the Maven plugin to generate a static event processor. The code below describes the calculations for an asset, see FluxtionBuilder

@SepBuilder(name = "SymbolTradeMonitor", packageName = "com.fluxtion.examples.tradingmonitor.generated.symbol", outputDir = "src/main/java", cleanOutputDir = true ) public void buildAssetAnalyser(SEPConfig cfg) { //entry points subsrcibe to events Wrapper<Deal> deals = select(Deal.class); Wrapper<AssetPrice> prices = select(AssetPrice.class); //result collector, and republish as an event source AssetTradePos results = cfg.addPublicNode(new AssetTradePos(), "assetTradePos"); eventSource(results); //calculate derived values Wrapper<Number> cashPosition = deals .map(multiply(), Deal::getSize, Deal::getPrice) .map(multiply(), -1) .map(cumSum()); Wrapper<Number> pos = deals.map(cumSum(), Deal::getSize); Wrapper<Number> mtm = pos.map(multiply(), arg(prices, AssetPrice::getPrice)); Wrapper<Number> pnl = add(mtm, cashPosition); //collect into results cashPosition.push(results::setCashPos); pos.push(results::setAssetPos); mtm.push(results::setMtm); pnl.push(results::setPnl); deals.map(count()).push(results::setDealsProcessed); prices.map(count()).push(results::setPricesProcessed); //add some rules - only fires on first breach pnl.filter(lt(-200)) .notifyOnChange(true) .map(count()) .push(results::setPnlBreaches); pos.filter(outsideBand(-200, 200)) .notifyOnChange(true) .map(count()) .push(results::setPositionBreaches); //human readable names to nodes in generated code - not required deals.id("deals"); prices.id("prices"); cashPosition.id("cashPos"); pos.id("assetPos"); mtm.id("mtm"); pnl.id("pnl"); }





AssetPrice and Deal events. Generated helper classes are used by the event processor to calculate the aggregates; the helper classes are The functional description is converted into an efficient imperative form for execution. A generated event processor, SymbolTradeMonitor , is the entry point forandevents. Generated helper classes are used by the event processor to calculate the aggregates; the helper classes are here

The processor receives events from the partitioner and invokes helper functions to extract data and call calculation functions, storing aggregate results in nodes. Aggregate values are pushed into fields of the results instance, AssetTradePos . No intermediate objects are created; any primitive calculation is handled without auto-boxing. Calculation nodes reference data from parent instances, no data objects are moved around the graph during execution. Once the graph is initialized, there are no memory allocations when an event is processed.

An image representing the processing graph for an asset calculation is generated at the same time as the code, which can be seen below:

A similar set of calculations is described for the portfolio in the FluxtionBuilderbuilder class buildPortfolioAnalyser method, generating a PortfolioTradeMonitor event.

The AssetTradePos is published from a SymbolTradeMonitor to the PortfolioTradeMonitor .

The generated files for the portfolio calculations are located here.

Partitioning and Gathering All calculations, partitioning and gathering operations happen in the same single thread; no locks are required. Immutable objects are not required as there are no concurrency issues to handle. The marshaled events have an isolated private scope, allowing safe re-use of instances as the generated event processors control the lifecycle of the instances during event processing.

System Data Flow

The diagram below shows the complete data flow for the system from bytes on a disk to the published summary report. The purple boxes are generated as part of the build; blue boxes are re-usable classes.

Conclusion

In this article, I have shown how it is possible to solve a complex event handling problem in Java with almost no waste. High-level functions were utilized in a declarative/functional approach to describing desired behavior and the generated event processors met the requirements of the description. A simple annotation triggered the marshaller generation. The generated code is a simple imperative code that the JIT can optimize easily. No unnecessary memory allocations are made, and instances are re-used as much as possible.

Following this approach, high-performance solutions with low resource consumption are within the grasp of the average programmer. Traditionally, only specialist engineers with many years of experience could achieve these results.

Although novel in Java, this approach is familiar in other languages, commonly known as zero-cost abstraction.

With today's cloud-based computing environments, resources are charged per unit consumed. Any solution that saves energy will also have a positive benefit on the company.

Hope you enjoyed!



