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## Chapter 14. Benchmarking and tweaking

## 14.1. Finding the best `Solver` configuration

OptaPlanner supports several optimization algorithms, but you're probably wondering which is the best one? Although some optimization algorithms generally perform better than others, it really depends on your problem domain. Most solver phases have parameters which can be tweaked. Those parameters can influence the results a lot, even though most solver phases work pretty well out-of-the-box.

Luckily, OptaPlanner includes a benchmarker, which allows you to play out different solver phases with different settings against each other, so you can pick the best configuration for your planning problem.

## 14.2. Doing a benchmark

### 14.2.1. Adding a dependency on optaplanner-benchmark

The benchmarker is in a separate artifact called `optaplanner-benchmark`.

If you use Maven, add a dependency in your `pom.xml` file:

        <dependency>

          <groupId>org.optaplanner</groupId>

          <artifactId>optaplanner-benchmark</artifactId>

        </dependency>

This is similar for Gradle, Ivy and Buildr. The version must be exactly the same as the `optaplanner-core` version used (which is automatically the case if you import `optaplanner-bom`).

If you use ANT, you've probably already copied the required jars from the download zip's `binaries` directory.

### 14.2.2. Building and running a `PlannerBenchmark`

Build a `PlannerBenchmark` instance with a `PlannerBenchmarkFactory`. Configure it with a benchmark configuration XML file, provided as a classpath resource:

            PlannerBenchmarkFactory plannerBenchmarkFactory = PlannerBenchmarkFactory.createFromXmlResource(

                    "org/optaplanner/examples/nqueens/benchmark/nqueensBenchmarkConfig.xml");

            PlannerBenchmark plannerBenchmark = benchmarkFactory.buildPlannerBenchmark();

            plannerBenchmark.benchmark();

A benchmark configuration file looks like this:

    <?xml version="1.0" encoding="UTF-8"?>

    <plannerBenchmark>

      <benchmarkDirectory>local/data/nqueens</benchmarkDirectory>

      <inheritedSolverBenchmark>

        <problemBenchmarks>

          ...

          <inputSolutionFile>data/nqueens/unsolved/32queens.xml</inputSolutionFile>

          <inputSolutionFile>data/nqueens/unsolved/64queens.xml</inputSolutionFile>

        </problemBenchmarks>

        <solver>

          ...<!-- Common solver configuration -->

        </solver>

      </inheritedSolverBenchmark>

      <solverBenchmark>

        <name>Tabu Search</name>

        <solver>

          ...<!-- Tabu Search specific solver configuration -->

        </solver>

      </solverBenchmark>

      <solverBenchmark>

        <name>Simulated Annealing</name>

        <solver>

          ...<!-- Simulated Annealing specific solver configuration -->

        </solver>

      </solverBenchmark>

      <solverBenchmark>

        <name>Late Acceptance</name>

        <solver>

          ...<!-- Late Acceptance specific solver configuration -->

        </solver>

      </solverBenchmark>

    </plannerBenchmark>

This `PlannerBenchmark` will try 3 configurations (Tabu Search, Simulated Annealing and Late Acceptance) on 2 data sets (32queens and 64queens), so it will run 6 solvers.

Every `<solverBenchmark>` element contains a solver configuration and one or more `<inputSolutionFile>` elements. It will run the solver configuration on each of those unsolved solution files. The element `name` is optional, because it is generated if absent. The `inputSolutionFile` is read by a SolutionFileIO (relative to the working directory).

## Note

Use a forward slash (`/`) as the file separator (for example in the element `<inputSolutionFile>`). That will work on any platform (including Windows).

Do not use backslash (`\`) as the file separator: that breaks portability because it does not work on Linux and Mac.

To lower verbosity, the common parts of multiple `<solverBenchmark>` elements are extracted to the `<inheritedSolverBenchmark>` element. Every property can still be overwritten per `<solverBenchmark>` element. Note that inherited solver phases such as `<constructionHeuristic>` or `<localSearch>` are not overwritten but instead are added to the tail of the solver phases list.

The benchmark report will be written in the directory specified the `<benchmarkDirectory>` element (relative to the working directory).

## Note

It's recommended that the `benchmarkDirectory` is a directory ignored for source control and not cleaned by your build system. This way the generated files are not bloating your source control and they aren't lost when doing a build. Usually that directory is called `local`.

If an `Exception` or `Error` occurs in a single benchmark, the entire Benchmarker will not fail-fast (unlike everything else in OptaPlanner). Instead, the Benchmarker will continue to run all other benchmarks, write the benchmark report and then fail (if there is at least 1 failing single benchmark). The failing benchmarks will be clearly marked as such in the benchmark report.

### 14.2.3. SolutionFileIO: input and output of Solution files

#### 14.2.3.1. `SolutionFileIO` interface

The benchmarker needs to be able to read the input files to load a `Solution`. Also, it might need to write the best `Solution` of each benchmark to an output file. For that it uses a class that implements the `SolutionFileIO` interface:

    public interface SolutionFileIO {

        String getInputFileExtension();

        String getOutputFileExtension();

        Solution read(File inputSolutionFile);

        void write(Solution solution, File outputSolutionFile);

    }

#### 14.2.3.2. `XStreamSolutionFileIO`: the default `SolutionFileIO`

By default, a benchmarker uses a `XStreamSolutionFileIO` instance to read and write solutions.

It's required to tell the benchmarker about your `Solution` class which is annotated with XStream annotations:

        <problemBenchmarks>

          <xStreamAnnotatedClass>org.optaplanner.examples.nqueens.domain.NQueens</xStreamAnnotatedClass>

          <inputSolutionFile>data/nqueens/unsolved/32queens.xml</inputSolutionFile>

          ...

        </problemBenchmarks>

Those input files need to have been written with a `XStreamSolutionFileIO` instance, not just any `XStream` instance, because the `XStreamSolutionFileIO` uses a customized `XStream` instance.

## Warning

XStream (and XML in general) is a very verbose format. Reading or writing very large datasets in this format can cause an `OutOfMemoryError` and performance degradation.

#### 14.2.3.3. Custom `SolutionFileIO`

Alternatively, implement your own `SolutionFileIO` implementation and configure it with the `solutionFileIOClass` element:

        <problemBenchmarks>

          <solutionFileIOClass>org.optaplanner.examples.machinereassignment.persistence.MachineReassignmentFileIO</solutionFileIOClass>

          <inputSolutionFile>data/machinereassignment/import/model_a1_1.txt</inputSolutionFile>

          ...

        </problemBenchmarks>

It's recommended that output files can be read as input files, which also implies that `getInputFileExtension()` and `getOutputFileExtension()` return the same value.

## Warning

A `SolutionFileIO` implementation must be thread-safe.

#### 14.2.3.4. Reading an input solution from a database (or other repository)

The benchmark configuration currently expects an `<inputSolutionFile>` element for each dataset. There are 2 ways to deal with this if your dataset is in a database or another type of repository:

- Extract the datasets from the database and serialize them to a local file (for example as XML with `XStreamSolutionFileIO`). Then use those files an `<inputSolutionFile>` elements.

- For each dataset, create a txt file that holds the unique id of the dataset. Write a custom `SolutionFileIO` that reads that identifier, connects to the database and extract the problem identified by that id. Configure those txt files as `<inputSolutionFile>` elements.

## Note

Local files are always faster and don't require a network connection.

### 14.2.4. Warming up the HotSpot compiler

Without a warm up, the results of the first (or first few) benchmarks are not reliable, because they will have lost CPU time on HotSpot JIT compilation (and possibly DRL compilation too).

The avoid that distortion, the benchmarker can run some of the benchmarks for a specified amount of time, before running the real benchmarks. Generally, a warm up of 30 seconds suffices:

    <plannerBenchmark>

      ...

      <warmUpSecondsSpentLimit>30</warmUpSecondsSpentLimit>

      ...

    </plannerBenchmark>

### 14.2.5. Benchmark blueprint: a predefined configuration

To quickly configure and run a benchmark for typical solver configs, use a `solverBenchmarkBluePrint` instead of `solverBenchmark`s:

    <?xml version="1.0" encoding="UTF-8"?>

    <plannerBenchmark>

      <benchmarkDirectory>local/data/nqueens</benchmarkDirectory>

      <warmUpSecondsSpentLimit>30</warmUpSecondsSpentLimit>

      <inheritedSolverBenchmark>

        <problemBenchmarks>

          <xStreamAnnotatedClass>org.optaplanner.examples.nqueens.domain.NQueens</xStreamAnnotatedClass>

          <inputSolutionFile>data/nqueens/unsolved/32queens.xml</inputSolutionFile>

          <inputSolutionFile>data/nqueens/unsolved/64queens.xml</inputSolutionFile>

          <problemStatisticType>BEST_SCORE</problemStatisticType>

        </problemBenchmarks>

        <solver>

          <solutionClass>org.optaplanner.examples.nqueens.domain.NQueens</solutionClass>

          <entityClass>org.optaplanner.examples.nqueens.domain.Queen</entityClass>

          <scoreDirectorFactory>

            <scoreDefinitionType>SIMPLE</scoreDefinitionType>

            <scoreDrl>org/optaplanner/examples/nqueens/solver/nQueensScoreRules.drl</scoreDrl>

            <initializingScoreTrend>ONLY_DOWN</initializingScoreTrend>

          </scoreDirectorFactory>

        </solver>

      </inheritedSolverBenchmark>

      <solverBenchmarkBluePrint>

        <solverBenchmarkBluePrintType>ALL_CONSTRUCTION_HEURISTIC_TYPES</solverBenchmarkBluePrintType>

      </solverBenchmarkBluePrint>

    </plannerBenchmark>

The following `SolverBenchmarkBluePrintType`s are supported:

- `ALL_CONSTRUCTION_HEURISTIC_TYPES`: Run all Construction Heuristic types (First Fit, First Fit Decreasing, Cheapest Insertion, ...).

### 14.2.6. Writing the output solution of the benchmark runs

The best solution of each benchmark run can be written to the in the `benchmarkDirectory`. By default, this is disabled, because the files are rarely used and considered bloat. Also, on large datasets, writing the best solution of each single benchmark can take quite some time and memory (causing an `OutOfMemoryError`), especially in a verbose format like XStream.

To write those solutions in the `benchmarkDirectory`, enable `writeOutputSolutionEnabled`:

        <problemBenchmarks>

          ...

          <writeOutputSolutionEnabled>true</writeOutputSolutionEnabled>

          ...

        </problemBenchmarks>

### 14.2.7. Benchmark logging

Benchmark logging is configured like the `Solver` logging.

To separate the log messages of each single benchmark run into a separate file, use the MDC with key `singleBenchmark.name` in a sifting appender. For example with Logback in `logback.xml`:

      <appender name="fileAppender" class="ch.qos.logback.classic.sift.SiftingAppender">

        <discriminator>

          <key>singleBenchmark.name</key>

          <defaultValue>app</defaultValue>

        </discriminator>

        <sift>

          <appender name="fileAppender.${singleBenchmark.name}" class="...FileAppender">

            <file>local/log/optaplannerBenchmark-${singleBenchmark.name}.log</file>

            ...

          </appender>

        </sift>

      </appender>

## 14.3. Benchmark report

### 14.3.1. HTML report

After the running a benchmark, a HTML report will be written in the `benchmarkDirectory` with the filename `index.html`. Open it in your browser. It has a nice overview of your benchmark including:

- Summary statistics: graphs and tables

- Problem statistics per `inputSolutionFile`: graphs and CSV

- Each solver configuration (ranked): Handy to copy and paste

- Benchmark information: settings, hardware, ...

## Note

Graphs are generated by the excellent JFreeChart library.

The HTML report will use your default locale to format numbers. If you share the benchmark report with people from another country, consider overwriting the `locale` accordingly:

    <plannerBenchmark>

      ...

      <benchmarkReport>

        <locale>en_US</locale>

      </benchmarkReport>

      ...

    </plannerBenchmark>

### 14.3.2. Ranking the `Solver`s

The benchmark report automatically ranks the solvers. The `Solver` with rank `0` is called the favorite `Solver`: it performs best overall, but it might not be the best on every problem. It's recommended to use that favorite `Solver` in production.

However, there are different ways of ranking the solvers. Configure it like this:

    <plannerBenchmark>

      ...

      <benchmarkReport>

        <solverRankingType>TOTAL_SCORE</solverRankingType>

      </benchmarkReport>

      ...

    </plannerBenchmark>

The following `solverRankingType`s are supported:

- `TOTAL_SCORE` (default): Maximize the overall score, so minimize the overall cost if all solutions would be executed.

- `WORST_SCORE`: Minimize the worst case scenario.

- `TOTAL_RANKING`: Maximize the overall ranking. Use this if your datasets differ greatly in size or difficulty, producing a difference in `Score` magnitude.

You can also use a custom ranking, by implementing a `Comparator`:

      <benchmarkReport>

        <solverRankingComparatorClass>...TotalScoreSolverRankingComparator</solverRankingComparatorClass>

      </benchmarkReport>

Or by implementing a weight factory:

      <benchmarkReport>

        <solverRankingWeightFactoryClass>...TotalRankSolverRankingWeightFactory</solverRankingWeightFactoryClass>

      </benchmarkReport>

## 14.4. Summary statistics

### 14.4.1. Best score summary (graph and table)

Shows the best score per `inputSolutionFile` for each solver configuration.

Useful for visualizing the best solver configuration.

Figure 14.1. Best score summary statistic

### 14.4.2. Best score scalability summary (graph)

Shows the best score per problem scale for each solver configuration.

Useful for visualizing the scalability of each solver configuration.

## Note

The problem scale will report `0` if any `@ValueRangeProvider` method signature returns ValueRange (instead of `CountableValueRange` or `Collection`).

### 14.4.3. Winning score difference summary (graph and table)

Shows the winning score difference score per `inputSolutionFile` for each solver configuration. The winning score difference is the score difference with the score of the winning solver configuration for that particular `inputSolutionFile`.

Useful for zooming in on the results of the best score summary.

### 14.4.4. Worst score difference percentage (ROI) summary (graph and table)

Shows the return on investment (ROI) per `inputSolutionFile` for each solver configuration if you'd upgrade from the worst solver configuration for that particular `inputSolutionFile`.

Useful for visualizing the return on investment (ROI) to decision makers.

### 14.4.5. Average calculation count summary (graph and table)

Shows the score calculation speed: the average calculation count per second per problem scale for each solver configuration.

Useful for comparing different score calculators and/or score rule implementations (presuming that the solver configurations do not differ otherwise). Also useful to measure the scalability cost of an extra constraint.

### 14.4.6. Time spent summary (graph and table)

Shows the time spent per `inputSolutionFile` for each solver configuration. This is pointless if it's benchmarking against a fixed time limit.

Useful for visualizing the performance of construction heuristics (presuming that no other solver phases are configured).

### 14.4.7. Time spent scalability summary (graph)

Shows the time spent per problem scale for each solver configuration. This is pointless if it's benchmarking against a fixed time limit.

Useful for extrapolating the scalability of construction heuristics (presuming that no other solver phases are configured).

### 14.4.8. Best score per time spent summary (graph)

Shows the best score per time spent for each solver configuration. This is pointless if it's benchmarking against a fixed time limit.

Useful for visualizing trade-off between the best score versus the time spent for construction heuristics (presuming that no other solver phases are configured).

## 14.5. Statistic per dataset (graph and CSV)

### 14.5.1. Enabling a problem statistic

The benchmarker supports outputting problem statistics as graphs and CSV (comma separated values) files to the `benchmarkDirectory`. To configure one, add a `problemStatisticType` line:

    <plannerBenchmark>

      <benchmarkDirectory>local/data/nqueens/solved</benchmarkDirectory>

      <inheritedSolverBenchmark>

        <problemBenchmarks>

          ...

          <problemStatisticType>BEST_SCORE</problemStatisticType>

          <problemStatisticType>CALCULATE_COUNT_PER_SECOND</problemStatisticType>

        </problemBenchmarks>

        ...

      </inheritedSolverBenchmark>

      ...

    </plannerBenchmark>

Multiple `problemStatisticType` elements are allowed.

## Note

These statistic per dataset can slow down the solver noticeably, which affects the benchmark results. That's why they are optional and not enabled by default.

The non-optional summary statistics cannot slow down the solver noticeably.

The following types are supported:

### 14.5.2. Best score over time statistic (graph and CSV)

To see how the best score evolves over time, add:

        <problemBenchmarks>

          ...

          <problemStatisticType>BEST_SCORE</problemStatisticType>

        </problemBenchmarks>

Figure 14.2. Best score over time statistic

The best score over time statistic is very useful to detect abnormalities, such as a potential score trap.

## Note

A time gradient based algorithm (such as Simulated Annealing) will have a different statistic if it's run with a different time limit configuration. That's because this Simulated Annealing implementation automatically determines its velocity based on the amount of time that can be spent. On the other hand, for the Tabu Search and Late Annealing, what you see is what you'd get.

### 14.5.3. Step score over time statistic (graph and CSV)

To see how the step score evolves over time, add:

        <problemBenchmarks>

          ...

          <problemStatisticType>STEP_SCORE</problemStatisticType>

        </problemBenchmarks>

Figure 14.3. Step score over time statistic

Compare the step score statistic with the best score statistic (especially on parts for which the best score flatlines). If it hits a local optima, the solver should take deteriorating steps to escape it. But it shouldn't deteriorate too much either.

## Warning

The step score statistic has been seen to slow down the solver noticeably due to GC stress, especially for fast stepping algorithms (such as Simulated Annealing and Late Acceptance).

### 14.5.4. Calculate count per second statistic (graph and CSV)

To see how fast the scores are calculated, add:

        <problemBenchmarks>

          ...

          <problemStatisticType>CALCULATE_COUNT_PER_SECOND</problemStatisticType>

        </problemBenchmarks>

Figure 14.4. Calculate count per second statistic

## Note

The initial high calculate count is typical during solution initialization: it's far easier to calculate the score of a solution if only a handful planning entities have been initialized, than when all the planning entities are initialized.

After those few seconds of initialization, the calculate count is relatively stable, apart from an occasional stop-the-world garbage collector disruption.

### 14.5.5. Best solution mutation over time statistic (graph and CSV)

To see how much each new best solution differs from the previous best solution, by counting the number of planning variables which have a different value (not including the variables that have changed multiple times but still end up with the same value), add:

        <problemBenchmarks>

          ...

          <problemStatisticType>BEST_SOLUTION_MUTATION</problemStatisticType>

        </problemBenchmarks>

Figure 14.5. Best solution mutation over time statistic

Use Tabu Search - an algorithm that behaves like a human - to get an estimation on how difficult it would be for a human to improve the previous best solution to that new best solution.

### 14.5.6. Move count per step statistic (graph and CSV)

To see how the selected and accepted move count per step evolves over time, add:

        <problemBenchmarks>

          ...

          <problemStatisticType>MOVE_COUNT_PER_STEP</problemStatisticType>

        </problemBenchmarks>

Figure 14.6. Move count per step statistic

## Warning

This statistic has been seen to slow down the solver noticeably due to GC stress, especially for fast stepping algorithms (such as Simulated Annealing and Late Acceptance).

### 14.5.7. Memory use statistic (graph and CSV)

To see how much memory is used, add:

        <problemBenchmarks>

          ...

          <problemStatisticType>MEMORY_USE</problemStatisticType>

        </problemBenchmarks>

Figure 14.7. Memory use statistic

## Warning

The memory use statistic has been seen to affect the solver noticeably.

## 14.6. Statistic per single benchmark (graph and CSV)

### 14.6.1. Enabling a single statistic

A single statistic is a statics for 1 dataset for 1 solver configuration. Unlike a problem statistic, it does not aggregate over solver configurations.

The benchmarker supports outputting single statistics as graphs and CSV (comma separated values) files to the `benchmarkDirectory`. To configure one, add a `singleStatisticType` line:

    <plannerBenchmark>

      <benchmarkDirectory>local/data/nqueens/solved</benchmarkDirectory>

      <inheritedSolverBenchmark>

        <problemBenchmarks>

          ...

          <problemStatisticType>...</problemStatisticType>

          <singleStatisticType>PICKED_MOVE_TYPE_BEST_SCORE_DIFF</singleStatisticType>

        </problemBenchmarks>

        ...

      </inheritedSolverBenchmark>

      ...

    </plannerBenchmark>

Multiple `singleStatisticType` elements are allowed.

## Note

These statistic per single benchmark can slow down the solver noticeably, which affects the benchmark results. That's why they are optional and not enabled by default.

The following types are supported:

### 14.6.2. Constraint match total best score over time statistic (graph and CSV)

To see which constraints are matched in the best score (and how much) over time, add:

        <problemBenchmarks>

          ...

          <singleStatisticType>CONSTRAINT_MATCH_TOTAL_BEST_SCORE</singleStatisticType>

        </problemBenchmarks>

Figure 14.8. Constraint match total best score diff over time statistic

Requires the score calculation to support constraint matches. Drools score calculation supports constraint matches automatically, but incremental Java score calculation requires requires more work.

## Warning

The constraint match total statistics has been seen to affect the solver noticeably.

### 14.6.3. Constraint match total step score over time statistic (graph and CSV)

To see which constraints are matched in the step score (and how much) over time, add:

        <problemBenchmarks>

          ...

          <singleStatisticType>CONSTRAINT_MATCH_TOTAL_STEP_SCORE</singleStatisticType>

        </problemBenchmarks>

Figure 14.9. Constraint match total step score diff over time statistic

Requires the score calculation to support constraint matches. Drools score calculation supports constraint matches automatically, but incremental Java score calculation requires requires more work.

## Warning

The constraint match total statistics has been seen to affect the solver noticeably.

### 14.6.4. Picked move type best score diff over time statistic (graph and CSV)

To see which move types improve the best score (and how much) over time, add:

        <problemBenchmarks>

          ...

          <singleStatisticType>PICKED_MOVE_TYPE_BEST_SCORE_DIFF</singleStatisticType>

        </problemBenchmarks>

Figure 14.10. Picked move type best score diff over time statistic

### 14.6.5. Picked move type step score diff over time statistic (graph and CSV)

To see how much each winning step affects the step score over time, add:

        <problemBenchmarks>

          ...

          <singleStatisticType>PICKED_MOVE_TYPE_STEP_SCORE_DIFF</singleStatisticType>

        </problemBenchmarks>

Figure 14.11. Picked move type step score diff over time statistic

## 14.7. Advanced benchmarking

### 14.7.1. Benchmarking performance tricks

#### 14.7.1.1. Parallel benchmarking on multiple threads

If you have multiple processors available on your computer, you can run multiple benchmarks in parallel on multiple threads to get your benchmarks results faster:

    <plannerBenchmark>

      ...

      <parallelBenchmarkCount>AUTO</parallelBenchmarkCount>

      ...

    </plannerBenchmark>

## Warning

Running too many benchmarks in parallel will affect the results of benchmarks negatively. Leave some processors unused for garbage collection and other processes.

We tweak `parallelBenchmarkCount` `AUTO` to maximize the reliability and efficiency of the benchmark results.

The following `parallelBenchmarkCount`s are supported:

- `1` (default): Run all benchmarks sequentially.

- `AUTO`: Let Planner decide how many benchmarks to run in parallel. This formula is based on experience. It's recommended to prefer this over the other parallel enabling options.

- Static number: The number of benchmarks to run in parallel.

      <parallelBenchmarkCount>2</parallelBenchmarkCount>

- JavaScript formula: Formula for the number of benchmarks to run in parallel. It can use the variable `availableProcessorCount`. For example:

      <parallelBenchmarkCount>(availableProcessorCount / 2) + 1</parallelBenchmarkCount>

## Note

The `parallelBenchmarkCount` is always limited to the number of available processors. If it's higher, it will be automatically decreased.

## Note

In the future, we will also support multi-JVM benchmarking. This feature is independent of multi-threaded solving or multi-JVM solving.

### 14.7.2. Template based benchmarking and matrix benchmarking

Matrix benchmarking is benchmarking a combination of value sets. For example: benchmark 4 `entityTabuSize` values (`5`, `7`, `11` and `13`) combined with 3 `acceptedCountLimit` values (`500`, `1000` and `2000`), resulting in 12 solver configurations.

To reduce the verbosity of such a benchmark configuration, you can use a Freemarker template for the benchmark configuration instead:

    <plannerBenchmark>

      ...

      <inheritedSolverBenchmark>

        ...

      </inheritedSolverBenchmark>

    <#list [5, 7, 11, 13] as entityTabuSize>

    <#list [500, 1000, 2000] as acceptedCountLimit>

      <solverBenchmark>

        <name>entityTabuSize ${entityTabuSize} acceptedCountLimit ${acceptedCountLimit}</name>

        <solver>

          <localSearch>

            <unionMoveSelector>

              <changeMoveSelector/>

              <swapMoveSelector/>

            </unionMoveSelector>

            <acceptor>

              <entityTabuSize>${entityTabuSize}</entityTabuSize>

            </acceptor>

            <forager>

              <acceptedCountLimit>${acceptedCountLimit}</acceptedCountLimit>

            </forager>

          </localSearch>

        </solver>

      </solverBenchmark>

    </#list>

    </#list>

    </plannerBenchmark>

And build it with the class `PlannerBenchmarkFactory`:

            PlannerBenchmarkFactory plannerBenchmarkFactory = PlannerBenchmarkFactory.createFromFreemarkerXmlResource(

                    "org/optaplanner/examples/cloudbalancing/benchmark/cloudBalancingBenchmarkConfigTemplate.xml.ftl");

            PlannerBenchmark plannerBenchmark = benchmarkFactory.buildPlannerBenchmark();

### 14.7.3. Benchmark report aggregation

The `BenchmarkAggregator` takes 1 or more existing benchmarks and merges them into new benchmark report, without actually running the benchmarks again. This is useful to generate a:

- Code changes impact report: Run the same benchmark configuration before and after the code changes, then aggregate a report.

- Dependency upgrade impact report: Run the same benchmark configuration before and after upgrading the dependency, then aggregate a report.

- Condense a too verbose report: Select only the interesting solver benchmarks from the existing report. This especially useful on template reports to make the graphs readable.

- Partial rerun: Rerun part of an existing report (for example only the failed or invalid solvers), then recreate the original report with the new values.

To use it, provide a `PlannerBenchmarkFactory` to the `BenchmarkAggregatorFrame` to display the GUI:

        public static void main(String[] args) {

            PlannerBenchmarkFactory plannerBenchmarkFactory = PlannerBenchmarkFactory.createFromXmlResource(

                    "org/optaplanner/examples/nqueens/benchmark/nqueensBenchmarkConfig.xml");

            BenchmarkAggregatorFrame.createAndDisplay(plannerBenchmarkFactory);

        }

## Warning

Despite that it uses a benchmark configuration as input, it ignores all elements of that configuration, except for the elements `<benchmarkDirectory>` and `<benchmarkReport>`.

In the GUI, select the interesting benchmarks and click the button to generate the report.

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