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Tracing

Warning
This feature is experimental.

A number of APIs in Ignite are instrumented for tracing with OpenCensus. You can collect distributed traces of various tasks executed in your cluster and use this information to diagnose latency problems.

We suggest you get familiar with OpenCensus tracing documentation before reading this chapter: https://opencensus.io/tracing/.

The following Ignite APIs are instrumented for tracing:

  • Discovery

  • Communication

  • Exchange

  • Transactions

  • SQL queries

To view traces, you must export them into an external system. You can use one of the OpenCensus exporters or write your own, but in any case, you will have to write code that registers an exporter in Ignite. Refer to Exporting Traces for details.

Configuring Tracing

Enable OpenCensus tracing in the node configuration. All nodes in the cluster must use the same tracing configuration.

<bean class="org.apache.ignite.configuration.IgniteConfiguration">

    <property name="tracingSpi">
        <bean class="org.apache.ignite.spi.tracing.opencensus.OpenCensusTracingSpi"/>
    </property>

</bean>
IgniteConfiguration cfg = new IgniteConfiguration();

cfg.setTracingSpi(new org.apache.ignite.spi.tracing.opencensus.OpenCensusTracingSpi());

Ignite ignite = Ignition.start(cfg);
This API is not presently available for C++. You can use XML configuration.

Enabling Trace Sampling

When you start your cluster with the above configuration, Ignite does not collect traces. You have to enable trace sampling for a specific API at runtime. You can turn trace sampling on and off at will, for example, only for the period when you are troubleshooting a problem.

You can do this in two ways:

  • via the control script from the command line

  • programmatically

Traces are collected at a given probabilistic sampling rate. The rate is specified as a value between 0.0 and 1.0 inclusive: 0 means no sampling, 1 means always sampling.

When the sampling rate is set to a value greater than 0, Ignite collects traces. To disable trace collection, set the sampling rate to 0.

The following sections describe the two ways of enabling trace sampling.

Using Control Script

Go to the {IGNITE_HOME}/bin directory of your Ignite installation. Enable experimental commands in the control script:

export IGNITE_ENABLE_EXPERIMENTAL_COMMAND=true

Enable tracing for a specific API:

./control.sh --tracing-configuration set --scope TX --sampling-rate 1

Refer to the Control Script sections for the list of all parameters.

Programmatically

Once you start the node, you can enable trace sampling as follows:

Ignite ignite = Ignition.start();

ignite.tracingConfiguration().set(
        new TracingConfigurationCoordinates.Builder(Scope.TX).build(),
        new TracingConfigurationParameters.Builder().withSamplingRate(1).build());

The --scope parameter specifies the API you want to trace. The following APIs are instrumented for tracing:

  • DISCOVERY — discovery events

  • EXCHANGE — exchange events

  • COMMUNICATION — communication events

  • TX — transactions

  • SQL — SQL queries

The --sampling-rate is the probabilistic sampling rate, a number between 0 and 1:

  • 0 means no sampling,

  • 1 means always sampling.

Exporting Traces

To view traces, you need to export them to an external backend using one of the available exporters. OpenCensus supports a number of exporters out-of-the-box, and you can write a custom one. Refer to the OpenCensus Exporters for details.

In this section, we will show how to export traces to Zipkin.

  1. Follow this guide to launch Zipkin on your machine.

  2. Register ZipkinTraceExporter in the application where you start Ignite:

    //register Zipkin exporter
    ZipkinTraceExporter.createAndRegister(
            ZipkinExporterConfiguration.builder().setV2Url("http://localhost:9411/api/v2/spans")
                    .setServiceName("ignite-cluster").build());
    
    IgniteConfiguration cfg = new IgniteConfiguration().setClientMode(true)
            .setTracingSpi(new org.apache.ignite.spi.tracing.opencensus.OpenCensusTracingSpi());
    
    Ignite ignite = Ignition.start(cfg);
    
    //enable trace sampling for transactions with 100% sampling rate
    ignite.tracingConfiguration().set(
            new TracingConfigurationCoordinates.Builder(Scope.TX).build(),
            new TracingConfigurationParameters.Builder().withSamplingRate(1).build());
    
    //create a transactional cache
    IgniteCache<Integer, String> cache = ignite
            .getOrCreateCache(new CacheConfiguration<Integer, String>("myCache")
                    .setAtomicityMode(CacheAtomicityMode.TRANSACTIONAL));
    
    IgniteTransactions transactions = ignite.transactions();
    
    // start a transaction
    try (Transaction tx = transactions.txStart()) {
        //do some operations
        cache.put(1, "test value");
    
        System.out.println(cache.get(1));
    
        cache.put(1, "second value");
    
        tx.commit();
    }
    
    try {
        //This code here is to wait until the trace is exported to Zipkin.
        //If your application doesn't stop here, you don't need this piece of code.
        Thread.sleep(5_000);
    } catch (InterruptedException e) {
        e.printStackTrace();
    }
  3. Open http://localhost:9411/zipkin in your browser and click the search icon.

    This is what a trace of the transaction looks like:

    trace in zipkin

Analyzing Trace Data

A trace is recorded information about the execution of a specific event. Each trace consists of a tree of spans. A span is an individual unit of work performed by the system in order to process the event.

Because of the distributed nature of Ignite, an operation usually involves multiple nodes. Therefore, a trace can include spans from multiple nodes. Each span always contains the information about the node where the corresponding operation was executed.

In the image of the transaction trace presented above, you can see that the trace contains the spans associated with the following operations:

  • acquire locks (transactions.colocated.lock.map),

  • get (transactions.near.enlist.read),

  • put (transactions.near.enlist.write),

  • commit (transactions.commit), and

  • close (transactions.close).

The commit operation, in turn, consists of two operations: prepare and finish.

You can click on each span to view the annotations and tags attached to it.

Span

Tracing SQL Queries

To enable SQL queries tracing, use SQL as a value of the scope parameter during the trace sampling configuration. If tracing of SQL queries is enabled, execution of each SQL query on any cluster node will produce a separate trace.

Important

Enabling tracing for SQL queries imposes a severe degradation on SQL engine performance.

The table below provides descriptions, a list of tags, and annotations for each span that can be a part of the SQL query trace tree.

Note

Depending on the SQL query type and its execution plan, some spans may not be present in the SQL query span tree.

Span Name Description Tags and Annotations

sql.query

Execution of an SQL query from the moment of registration until the used resources on the query initiator node are released

  • sql.query.text - SQL query text

  • sql.schema - SQL schema

sql.cursor.open

SQL query cursor opening

sql.cursor.close

SQL query cursor closure

sql.cursor.cancel

SQL query cursor cancellation

sql.query.parse

Parsing of SQL query

  • sql.parser.cache.hit - Whether parsing of the SQL query was skipped due to the cached result

sql.query.execute.request

Processing of SQL query execution request

  • sql.query.text - SQL query text

sql.next.page.request

Processing of the request for obtaining the next page of local SQL query execution result

sql.page.response

Processing of the message with a node local SQL query execution result page

sql.query.execute

Execution of query by H2 SQL engine

  • sql.query.text - SQL query text

sql.page.prepare

Reading rows from the cursor and preparing a result page

  • sql.page.rows - Number of rows that a result page contains

sql.fail.response

Processing of a message that indicates failure of SQL query execution

sql.dml.query.execute.request

Processing of SQL DML query execution request

  • sql.query.text - SQL query text

sql.dml.query.response

Processing of SQL DML query execution result by query initiator node

sql.query.cancel.request

Processing of SQL query cancel request

sql.iterator.open

SQL query iterator opening

sql.iterator.close

SQL query iterator closure

sql.page.fetch

Fetching SQL query result page

  • sql.page.rows - Number of rows that result page contains

sql.page.wait

Waiting for SQL query results page to be received from remote node

sql.index.range.request

Processing SQL index range request

  • sql.index - SQL index name

  • sql.table - SQL table name

  • sql.index.range.rows - Number of rows that an index range request result contains

sql.index.range.response

Processing SQL index range responce

sql.dml.query.execute

Execution of SQL DML query

sql.command.query.execute

Execution of an SQL command query, which is either a DDL query or an Ignite native command

sql.partitions.reserve

Reservation of data partitions used to execute a query

  • Annotation message that indicates reservation of data partitions for a particular cache - Cache partitions were reserved [cache=<name of the cache>, partitions=[<partitions numbers>]

sql.cache.update

Cache update as a result of SQL DML query execution

  • sql.cache.updates - Number of cache entries to be updated as a result of DML query

sql.batch.process

Processing of SQL batch update