title: Prometheus weight: 5 type: docs aliases:

/dev/connectors/prometheus.html
/apis/streaming/connectors/prometheus.html

Prometheus Sink

This sink connector can be used to write data to Prometheus-compatible storage, using the Remote Write Prometheus interface.

The Prometheus-compatible backend must support Remote Write 1.0 standard API, and the Remote Write endpoint must be enabled.

{{< hint warn >}}This connector is not meant for sending internal Flink metrics to Prometheus. To publish Flink metrics, for monitoring health and operations of the Flink cluster, you should use Metric Reporters.{{< /hint >}}

To use the connector, add the following Maven dependency to your project:

Usage

The Prometheus sink provides a builder class to build a PrometheusSink instance. The code snippets below shows how to build a PrometheusSink with a basic configuration, and an optional request signer.

PrometheusSink sink = PrometheusSink.builder()
        .setPrometheusRemoteWriteUrl(prometheusRemoteWriteUrl)
        .setRequestSigner(new AmazonManagedPrometheusWriteRequestSigner(prometheusRemoteWriteUrl, prometheusRegion)) // Optional
        .build();

The only required configuration is prometheusRemoteWriteUrl. All other configurations are optional.

If your sink has parallelism > 1, you need to ensure the stream is keyed using the PrometheusTimeSeriesLabelsAndMetricNameKeySelector key selector, so that all samples of the same time-series are in the same partition and order is not lost. See Sink parallelism and keyed streams for more details.

Input data objects

The sink expects PrometheusTimeSeries records as input. Your input data must be converted into PrometheusTimeSeries, using a map or flatMap operator, before the sending to the sink.

PrometheusTimeSeries instances are immutable and cannot be reused. You can use the builder to create and populate instances.

A PrometheusTimeSeries represents a single time-series record when sent to the Remote Write interface. Each time-series record may contain multiple samples.

{{< hint info >}} In the context of Prometheus, the term “time-series” is overloaded. It means both a series of samples with a unique set of labels (a time-series in the underlying time-series database), and a record sent to the Remote Write interface. A PrometheusTimeSeries instance represents a record sent to the interface.

The two concepts are related, because time-series “records” with the same sets of labels are sent to the same “database time-series”.{{< /hint >}}

Each PrometheusTimeSeries record contains:

One metricName. A string that is translated into the value of the __name__ label.
Zero or more Label entries. Each label has a key and a value, both String. Labels represent additional dimensions of the samples. Duplicate Label keys are not allowed.
One or more Sample. Each sample has a value (double) representing the measure, and a timestamp (long) representing the time of the measure, in milliseconds from the Epoch. Duplicate timestamps in the same record are not allowed.

The following pseudocode represents the structure of a PrometheusTimeSeries record:

PrometheusTimeSeries
  + --> metricName <String>
  + --> Label [0..*]
            + name <String>
            + value <String>
  + --> Sample [1..*]
            + timestamp <long>
            + value <double>

{{< hint info >}}The set of Labels and metricName are the unique identifiers of the database time-series. A composite of all Labels and metricName is also the key you should use to partition data, both inside the Flink application and upstream, to guarantee ordering per time-series is retained.{{< /hint >}}

Populating a PrometheusTimeSeries

PrometheusTimeSeries provides a builder interface.

PrometheusTimeSeries inputRecord =
        PrometheusTimeSeries.builder()
                .withMetricName(metricName)
                .addLabel("DeviceID", instanceId)
                .addLabel("RoomID", roomId)
                .addSample(measurement1, time1)
                .addSample(measurement2, time2)
                .build();

Each PrometheusTimeSeries instance can contain multiple samples. Call .addSample(...) for each of them. The order in which samples are added is retained. The maximum number of samples per record is limited by the maxBatchSizeInSamples configuration.

Aggregating multiple samples into a single PrometheusTimeSeries record may improve write performances.

Prometheus remote-write constraints

Prometheus imposes strict constraints on data format and on ordering. Any write request containing records that violate these constraints is rejected.

See Remote Write specification for details about these constraints.

In practice, the behavior when writing data to a Prometheus-compatible backend depends on the Prometheus implementation and configuration. In some cases, these constraints are relaxed, and writes violating the Remote Write specifications may be accepted.

For this reason, this connector does not enforce any data constraints directly. The user is responsible for sending data to the sink that does not violate the actual constraints of your Prometheus implementation. See User responsibilities for more details.

Ordering constraints

Remote Write specifications require multiple ordering constraints:

Labels within a PrometheusTimeSeries record must be in lexicographical order by key.
Samples within a PrometheusTimeSeries record must be in timestamp order, from older to newer.
All samples belonging to the same time-series (a unique set of labels and metricName) must be written in timestamp order.
Within the same time-series, duplicate samples with the same timestamp are not allowed

When the Prometheus-compatible backend implementation supports out-of-order time windows and the option is enabled, sample ordering constraint is relaxed. You can send out of order data within the configured window.

Format constraints

The PrometheusTimeSeries records sent to the sink must also respect the following constraints:

metricName must be defined and non-empty. The connector translates this property into the value of the __name__ label.
Label names must follow the regex [a-zA-Z:_]([a-zA-Z0-9_:]). In particular, labels names containing @, $, !, . (dot), or any punctuation marks (except colon : and hyphen -) are not valid.
Label names must not begin with __ (double underscore). These label names are reserved.
No duplicate Label names are allowed.
Label values and metricName may contain any UTF-8 character.
Label values cannot be empty (null or empty string).

The PrometheusTimeSeries builder does not enforce these constraints.

User responsibilities

The user is responsible for sending records to the sink (PrometheusTimeSeries) that respect format and ordering constraints required by your Prometheus implementation. The connector does not perform any validation or reordering.

Sample ordering by timestamp is particularly important. Samples belonging to the same time-series, i.e. with the same set of Labels and the same metric name, must be written in timestamp order. Source data must be generated in order. The order must also be retained before the sink. When partitioning the data, records with same set of labels and metric name must be sent to the same partition in order to retain ordering.

Malformed or out of order records written to the Remote Write endpoint are rejected and dropped by the sink. This may cause data loss.

Any record violating ordering sent to the sink is dropped and may cause other records batched in the same write-request to be dropped. For more details, see Connector guarantees.

Sink parallelism and keyed streams

Each sink operator sub-task uses a single thread to send write requests to the Remote Write endpoint, and PrometheusTimeSeries records are written in the same order as they are received by the sub-task.

To ensure all records belonging to the same time-series (i.e. PrometheusTimeSeries with identical list of Label and metricName) are written by the same sink subtask, the stream of PrometheusTimeSeries must be keyed using PrometheusTimeSeriesLabelsAndMetricNameKeySelector.

DataStream<MyRecord> inputRecords;
// ...
KeyedStream<PrometheusTimeSeries> timeSeries = inputRecords
        .map(new MyRecordToTimeSeriesMapper())
        .keyBy(new PrometheusTimeSeriesLabelsAndMetricNameKeySelector());

timeSeries.sinkTo(prometheusSink);

Using this key selector prevents accidental out-of-orderness due to repartitioning before the sink operator. However, the user is responsible to retain ordering to this point by partitioning the records correctly.

Error handling

This paragraph covers handling of errors conditions when writing data to the Remote Write endpoint.

There are four types of error conditions:

Retryable errors due to temporary error conditions in the Remote-Write server or due to throttling: 5xx or 429 http responses, connectivity issues.
Non-retryable errors due to data violating any of the constraints, malformed data or out-of-order samples: 4xx http responses, except 429, 403 and 404.
Fatal error response: authentication failures (403 http response) or incorrect endpoint path (404 http response).
Any other unexpected failure while writing, due to exceptions while writing to the Prometheus endpoint.

On-error behaviors

When any of the above error is encountered, the connector implements one of these two behaviors:

FAIL: throw an unhandled exception, the job fails
DISCARD_AND_CONTINUE: discard the request that caused the error, and continue with the next record.

When a write request is discarded on DISCARD_AND_CONTINUE, all the following happens:

Log a message at WARN level with the cause of the error. When the error is caused by a response from the endpoint, the payload of the response from the endpoint is included.
Increase counter metrics, to count the number of rejected samples and write requests.
Drop the entire write request. Note that due to batching, a write request may contain multiple PrometheusTimeSeries.
Continue with the next input record.

{{< hint info >}}Prometheus Remote Write does not support partial failures. Due to batching, a single write request may contain multiple input records (PrometheusTimeSeries). If a request contains even a single offending record, the entire write request (the entire batch) must be discarded.{{< /hint >}}

Retryable error responses

A typical retryable error condition is endpoint throttling, with a 429, Too Many Requests response.

On retryable errors, the connector will retry, using a configurable backoff strategy. When the maximum number of retries is exceeded, the write request fails. What happens at this point depends on the onMaxRetryExceeded error handling configuration.

If onMaxRetryExceeded is FAIL (default): the job fails and restarts from checkpoint.
If onMaxRetryExceeded is DISCARD_AND_CONTINUE: the entire write request is dropped and the sink continues with the next record.

Non-retryable error responses

A typical non-retryable condition is due to malformed or out of order samples that are rejected by Prometheus with a 400, Bad Request response.

When such an error is received, the connector applies the DISCARD_AND_CONTINUE behavior. This behavior is currently not configurable.

Fatal error responses

403, Forbidden responses, caused by incorrect or missing authentication, and 404, Not Found responses, caused by incorrect endpoint URL, are always considered fatal. Behavior is always FAIL and not configurable.

Other I/O errors

Any I/O error that happens in the http client is also fatal (behavior is always FAIL, not configurable).

Error handling configuration

The error handling behavior is partially configurable. You can configure the behavior when building the instance of the sink.

PrometheusSink sink = PrometheusSink.builder()
        // ...    
        .setErrorHandlingBehaviorConfiguration(
                SinkWriterErrorHandlingBehaviorConfiguration.builder()
                    .onMaxRetryExceeded(OnErrorBehavior.DISCARD_AND_CONTINUE)
                    .onPrometheusNonRetryableError(OnErrorBehavior.DISCARD_AND_CONTINUE)
                    .build())
        .build();

At the moment, the only supported configuration, onMaxRetryExceeded, controls the behavior when the maximum number of retries, after a retryable error, is exceeded. The default behavior is FAIL.

The configuration also allows setting the behavior on Prometheus non-retryable error (onPrometheusNonRetryableError), but the only allowed value at the moment is DISCARD_AND_CONTINUE.

Retry configuration

When a retryable error condition is encountered, the sink retries with an exponential backoff strategy.

The retry strategy can be configured with the following parameters:

initialRetryDelayMS: (default 30 millis) Initial retry delay. The retry delay doubles on every subsequent retry, up to the maximum retry delay.
maxRetryDelayMS: (default 5000 millis) Maximum retry delay. When this delay is reached, every subsequent retry has the same delay. Must be bigger than InitialRetryDelayMS
maxRetryCount: (default 100) Maximum number of retries for a single write request. Set max retries to Integer.MAX_VALUE if you want to (practically) retry forever.

When maxRetryCount is exceeded, the connector stops retrying. What happens at this point depends on the onMaxRetryExceeded error handling behavior.

You can configure the retry strategy when building the sink.

PrometheusSink sink = PrometheusSink.builder()
        // ...
        .setRetryConfiguration(
                RetryConfiguration.builder()
                    .setInitialRetryDelayMS(10L)
                    .setMaxRetryDelayMS(5000L)              
                    .setMaxRetryCount(Integer.MAX_VALUE)                  
                .build())
        // ...    
        .build();

Batching

To optimize write throughput, the sink batches writes. Multiple PrometheusTimeSeries are batched in a single write request to the Remote Write endpoint.

Batching is based on the number of samples per request, and a max buffering time.

The connector starts with writing a single PrometheusTimeSeries per write request. If the write succeeds, the batch size is increased up to a configurable limit of samples per request. The actual number of PrometheusTimeSeries per request may vary, because PrometheusTimeSeries may contain a variable number of samples.

Buffered PrometheusTimeSeries are stored in Flink state, and are not lost on application restart.

Batching can be controlled using the following parameters:

maxBatchSizeInSamples: (default: 500) max number of samples in a write request.
maxTimeInBufferMS: (default: 5000 millis) Max time input PrometheusTimeSeries are buffered before emitting the write request.
maxRecordSizeInSamples: (default: 500) max number of samples in a single PrometheusTimeSeries. It must be less or equal MaxBatchSizeInSamples.

{{< hint warn >}}If a record containing more samples than maxRecordSizeInSamples is encountered, the sink throws an exception causing the job to fail and restart from checkpoint, putting the job in an endless loop.{{< /hint >}}

You can configure batching when building the sink:

PrometheusSink sink = PrometheusSink.builder()
        .setMaxBatchSizeInSamples(100)
        .setMaxTimeInBufferMS(10000)
        // ...    
        .build();

Larger batches improve write performance but also increase the number of records potentially lost when a write request is rejected, due to DISCARD_AND_CONTINUE behavior. Reducing maxBatchSizeInSamples can help minimize data loss in this case, but may heavily reduce the throughput that Prometheus can ingest. The default maxBatchSizeInSamples of 500 maximizes the ingestion throughput.

Request Signer

Remote Write specification does not specify any authentication scheme. Authentication is delegated to the specific Prometheus-compatible backend implementation.

The connector allows to specify a request signer that can add headers to the http request. These headers can be based on the request body or any of the existing headers. This allows to implement authentication schemes that requires passing authentication or signature tokens in headers.

A request signer must implement the PrometheusRequestSigner interface.

public interface PrometheusRequestSigner extends Serializable {
  void addSignatureHeaders(Map<String, String> requestHeaders, byte[] requestBody);
}

Refer to the JavaDoc or the source code for more details.

A request signer can be added to the sink in the builder:

PrometheusSink sink = PrometheusSink.builder()
        // ...    
        .setRequestSigner(requestSigner)
        .build();

Amazon Managed Prometheus request signer

An implementation of PrometheusRequestSigner supporting Amazon Managed Prometheus (AMP) is provided.

An additional dependency is required to use the AMP request signer:

{{< connector_artifact flink-connector-prometheus prometheus-request-signer-amp >}}

The AMP signer retrieves AWS credentials using DefaultCredentialsProvider. Credentials are retrieved and used to sign every request to the endpoint.

You can add the AMP request signer to the sink:

PrometheusSink sink = PrometheusSink.builder()
        // ...    
        .setRequestSigner(new AmazonManagedPrometheusWriteRequestSigner(prometheusRemoteWriteUrl, prometheusRegion))
        .build();

HTTP client configuration

You can configure the HTTP client that sends write requests to the Remote Write endpoint.

socketTimeoutMs: (default: 5000 millis) HTTP client socket timeout
httpUserAgent: (default: Flink-Prometheus) User-Agent header

PrometheusSink sink = PrometheusSink.builder()
        // ...    
        .setSocketTimeoutMs(5000)
        .setHttpUserAgent(USER_AGENT)
        .build();

Connector metrics

The connector exposes custom metrics, counting data successfully written to the endpoint, and data dropped due to DISCARD_AND_CONTINUE.

Metric name	Description
`numSamplesOut`	Count of samples successfully written to Prometheus
`numWriteRequestsOut`	Count of samples successfully written to Prometheus
`numWriteRequestsRetries`	Count of write requests retries, due to retryable errors (e.g. throttling)
`numSamplesDropped`	Count of samples that have been dropped (data loss!) due to `DISCARD_AND_CONTINUE`
`numSamplesNonRetryableDropped`	Count of samples that have been dropped (data loss!) due to `onPrometheusNonRetryableError` set to `DISCARD_AND_CONTINUE` (default)
`numSamplesRetryLimitDropped`	Count of samples that have been dropped (data loss!) due to `onMaxRetryExceeded` set to `DISCARD_AND_CONTINUE`, when the retry limit was exceeded
`numWriteRequestsPermanentlyFailed`	Count of write requests permanently failed, due to any reasons

{{< hint info >}}The numByteSend metric should be ignored. This metric does not actually measure bytes, due to limitations of AsyncSink this connector is based on. Use numSamplesOut and numWriteRequestsOut to monitor the actual output of the sink.{{< /hint >}}

The metric group name is “Prometheus” by default. It can be changed:

PrometheusSink sink = PrometheusSink.builder()
        // ...    
        .setMetricGroupName("my-metric-group")
        .build();

Connector guarantees

The connector provides at-most-once guarantees. Data loss can happen, in particular if input data is malformed or out of order.

Data may be lost due to the DISCARD_AND_CONTINUE on-error behavior. This behavior may optionally be enabled when the maximum number of retries is exceeded, but is always enabled when non-retryable error conditions are encountered.

This behavior is due to the design of Prometheus Remote Write interface, that does not allow out-of-order writes in the same time-series. To prevent from putting the job in an endless loop of fail and restart from checkpoint, when out of order data is encountered, is to discard and continue. Out of order writes also happen when the job restart from checkpoint. Without discard and continue the sink would not allow the job to recover from checkpoint at all.

At the same time, Prometheus imposes per time-series ordering by timestamp. The sink guarantees the order is retained per partition. Key-by using PrometheusTimeSeriesLabelsAndMetricNameKeySelector guarantees that the input is partitioned by time-series, and no accidental reordering happens during the write. The user is responsible to partition data before the sink to ensure order is retained.

Example application

You can find a complete application demonstrating the configuration and usage of this sink in the tests of the connector.

Check out the source of org.apache.flink.connector.prometheus.sink.examples.DataStreamExample.

This class contains a full application that generates random data internally and writes to Prometheus.