Waste-Free Coding: Zero-Cost Abstraction in Java

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

Deal,symbol,size,price
AssetPrice,symbol,price
AssetPrice,FORD,15.0284
AssetPrice,APPL,16.4255
Deal,AMZN,-2000,15.9354

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:

1. 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.
2. 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

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

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  CharEvents 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  CharSequences 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

    @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");
    }

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.

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.

System Data Flow

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!