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Amazon Timestream Prometheus Connector

The Prometheus Connector receives and sends time series data between Prometheus and Amazon Timestream through Prometheus' remote write and remote read protocols.

Table of contents

Prerequisites

  1. Sign up for AWS — Before beginning, have an AWS account. For more information about creating an AWS account and retrieving your AWS credentials, see Signing Up for AWS.
  2. Amazon Timestream — Have databases and tables created on Amazon Timestream. To create databases and tables on Amazon Timestream, see Accessing Timestream.
  3. Minimum requirements — The Amazon Timestream Prometheus Connector for Go requires Go 1.14 or later.
  4. Prometheus — Download Prometheus from their Download page. To learn more about Prometheus, see their introduction documentation.
  5. Docker — Docker is only required when building or running the Docker image. To download Docker, see Get Started with Docker.

User Documentation

Quick Start

The following steps use one-click deployment to deploy the connector as a Lambda function along with an API Gateway.

  1. Prerequisites are met.
  2. Prometheus is configured, minimum version 2.0.0.
  3. Deploy with one click deployment: serverless/DEVELOPER_README.md#deployment.
  4. Update remote_read and remote_write values in prometheus.yml to the resources created by the deployment: serverless/DEVELOPER_README.md#configure-prometheus.
  5. Verify the Prometheus connector is working.

Advanced Options

The Prometheus Connector is available in the following formats:

Prometheus Configuration

To configure Prometheus to read and write to remote storage, configure the remote_read and remote_write sections in prometheus.yml. To learn more, see the remote read and remote write sections on Prometheus' configuration page.

  1. Configure Prometheus' remote read and remote write destination by setting the url options to the Prometheus Connector's listening URLs, e.g. "http://localhost:9201/write".

  2. Configure the basic authentication header for Prometheus read and write requests with valid IAM credentials.

    NOTE: All configuration options are case-sensitive, and session_token authentication parameter is not supported for MFA authenticated AWS users.

    basic_auth:
  username: accessKey
  password: secretAccessKey

    Prometheus also supports passing the password as a file, the following example has the IAM secret access key stored in secret.txt in the credentials folder:

    basic_auth:
  username: accessKey
  password_file: /Users/user/Desktop/credentials/secretAccessKey.txt

    The password_file path must be the absolute path for the file, and the password file must contain only the value for the aws_secret_access_key.

    NOTE: As a security best practice, it is recommended to regularly rotate IAM user access keys.

  3. With the default configuration, Prometheus uses TLS version 1.2 for remote_read and remote_write requests. It is recommended to secure the Prometheus requests with mutual TLS encryption in a production environment. This can be achieved by specifying the certificate authority file in the tls_config section for Prometheus' remote read and remote write configuration. To generate self-signed certificates during development see the Creating Self-signed TLS Certificates section.

    Here is an example of remote_write and remote_read configuration with TLS, where RootCA.pem is within the same directory as the Prometheus configuration file:

    NOTE: All configuration options are case-sensitive, and session_token authentication parameter is not supported for MFA authenticated AWS users.

    remote_write:
  - url: "https://localhost:9201/write"

    tls_config:
      # Ensure ca_file is a valid file path pointing to the CA certificate.
      ca_file: RootCA.pem

    basic_auth:
      username: accessKey
      password: secretAccessKey

remote_read:
  - url: "https://localhost:9201/read"

    basic_auth:
        username: accessKey
        password: secretAccessKey

    tls_config:
        # Ensure ca_file is a valid file path pointing to the CA certificate.
        ca_file: RootCA.pem

    See a full example without TLS configuration in simple-example.yml.

Run with One-Click Deployment

See serverless/DEVELOPER_README.md for serverless deployments, which includes one-click deployment links for CloudFormation.

This is the easiest and recommended method for running the connector.

Run with Precompiled Binaries

The pre-compiled binaries independent of platform will have the name bootstrap to align with the provided.al2023 lambda runtime naming convention. Run the precompiled binaries with required arguments default-database, default-table. Specify the region argument if your Timestream database is not in us-east-1, as that is the default value for the target region.

./bootstrap --default-database=prometheusDatabase --default-table=prometheusMetricsTable --region=us-west-2

It is recommended to secure the Prometheus requests with TLS encryption. To enable TLS encryption:

  1. Specify the server certificate and the server private key through the tls-certificate and tls-key configuration options. An example for macOS is as follows:

    ./bootstrap \
--default-database=prometheusDatabase \
--default-table=prometheusMetricsTable \
--region=us-west-2 \
--tls-certificate=serverCertificate.crt \
--tls-key=serverPrivateKey.key

  2. Ensure the certificate authority file has been specified in the tls_config section within Prometheus' configuration file, see Prometheus Configuration for an example.

To generate self-signed certificates during development see Creating Self-signed TLS Certificates.

For more examples on configuring the Prometheus Connector see Configuration Options.

Running Precompiled Binary for macOS

The following error message may show up when running the precompiled binary on macOS:

"bootstrap" cannot be opened because the developer cannot be verified.

Follow these steps to resolve:

  1. Choose Apple menu > System Preferences.
  2. Select Security & Privacy.
  3. Under the General tab, select Open Anyway.

Run with Docker Image

Load the Docker image by the following command and replace the value for appropriately:

docker load < timestream-prometheus-connector-docker-image-<version>.tar.gz

  • Linux and macOS

    docker run \
-p 9201:9201 \
timestream-prometheus-connector-docker \
--default-database=prometheusDatabase \
--default-table=prometheusMetricsTable

  • Windows

    docker run ^
-p 9201:9201 ^
timestream-prometheus-connector-docker ^
--default-database=prometheusDatabase ^
--default-table=prometheusMetricsTable

It is recommended to secure the Prometheus requests with HTTPS with TLS encryption. To enable TLS encryption:

  1. Mount the volume containing the server certificate and the server private key to a volume on the Docker container, then specify the path to the certificate and the key through the tls-certificate and tls-key configuration options. Note that the path specified must be with respect to the Docker container.

    In the following examples, server certificate and server private key are stored in the $HOME/tls directory on Linux and macOS or %USERPROFILE%/tls on Windows, but are mounted to /root/tls on the Docker container:

    • Linux and macOS

      docker run \
-v $HOME/tls:/root/tls:ro \
-p 9201:9201 \
timestream-prometheus-connector-docker \
--default-database=prometheusDatabase \
--default-table=prometheusMetricsTable \
--tls-certificate=/root/tls/serverCertificate.crt \
--tls-key=/root/tls/serverPrivateKey.key

    • Windows

      docker run ^
-v "%USERPROFILE%/tls:/root/tls/:ro" ^
-p 9201:9201 ^
timestream-prometheus-connector-docker ^
--default-database=prometheusDatabase ^
--default-table=prometheusMetricsTable ^
--tls-certificate=/root/tls/serverCertificate.crt ^
--tls-key=/root/tls/serverPrivateKey.key

  2. Ensure the certificate authority file has been specified in the tls_config section within Prometheus' configuration file, see Prometheus Configuration for an example.

To generate self-signed certificates during development see Creating Self-signed TLS Certificates.

To configure the web.listen-address option when running the Prometheus Connector through a Docker image, use the -p flag to expose the custom endpoint. The following example listens on the custom endpoint localhost:3080:

  • Linux and macOS

    docker run \
-p 3080:3080 \
timestream-prometheus-connector-docker \
--default-database=prometheusDatabase \
--default-table=prometheusMetricsTable \
--web.listen-address=:3080

  • Windows

    docker run ^
-p 3080:3080 ^
timestream-prometheus-connector-docker ^
--default-database=prometheusDatabase ^
--default-table=prometheusMetricsTable ^
--web.listen-address=:3080

For more information regarding the -p flag see the official Docker documentation.

Run with AWS Lambda

Running the Prometheus Connector on AWS Lambda allows for a serverless workflow. This section details the steps to configuring the IAM permissions to integrate the Prometheus Connector with Amazon API Gateway and AWS Lambda.

IAM Role and Policy Configuration

  1. Open the AWS management console for IAM.

  2. Click on Policies and select Create policy.

  3. Select the JSON tab and paste the following policy to provide basic permissions for the Lambda function to output logs to Cloudwatch:

     {
     "Version": "2012-10-17",
     "Statement": [
         {
             "Effect": "Allow",
             "Action": [
                 "logs:CreateLogGroup",
                 "logs:CreateLogStream",
                 "logs:PutLogEvents"
             ],
             "Resource": "arn:aws:logs:${AWS::Region}:${AWS::AccountId}:log-group:/aws/lambda/${LambdaFunctionName}:*"
         }
     ]
 }

  4. Update the values for ${AWS::Region}, ${AWS::AccountId}, ${LambdaFunctionName} in the following policy. An example of the updated value for Resource would be: "Resource": "arn:aws:logs:us-east-1:12345678:log-group:/aws/lambda/timestream-prometheus-connector:*".

  5. Click Next.

  6. Enter a policy name.

  7. Click on Create policy

  8. Click on Roles.

  9. Click on Create role

  10. Click on Custom trust policy.

  11. Paste the following to Policy Document to allow API gateway to access the policy.

    {
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": [
          "apigateway.amazonaws.com",
          "lambda.amazonaws.com"
        ]
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

  12. Click on Next

  13. Select the policy that you previously created which gives the lambda function basic permissions to output logs to Cloudwatch.

  14. Click Next.

  15. Enter a name for the role, in this example will use LambdaTimestreamFullAccessRole.

  16. Click on Create role.

  17. After creating the role successfully, click Roles under Access management and choose the role created to see the details.

  18. Take note of the Role ARN, this is required when creating a policy to allow the current user access to the role.

To provide access to this newly-created role, add a permission to the current user with the following steps:

  1. Open the AWS management console for IAM.

  2. Under Access management, click Users.

  3. Select the user that needs access to this role.

  4. Click Add permissions.

  5. Select Attach existing policies directly.

  6. Click Create policy.

  7. Switch to the JSON tab, and paste the following to provide the user with the permission to PassRole on the newly-created role, where role_arn can be on the Summary page of the newly-created role:

    {
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "PolicyStatementToAllowUserToPassOneSpecificRole",
            "Effect": "Allow",
            "Action": [
                "iam:PassRole"
            ],
            "Resource": "role_arn"
        }
    ]
}

  8. Click Next.

  9. Enter a policy name.

  10. Click Create policy.

  11. Attach the policy to the user.

Create the AWS Lambda Function

  1. Open the AWS management console for AWS Lambda.
  2. Click Create function.
  3. Enter a function name, this example will use PrometheusConnector as the function name.
  4. Choose Amazon Linux 2023 from the Runtime dropdown.
  5. Expand Change default execution role.
  6. Select Use an existing role.
  7. Choose the newly-created role from the dropdown, for this example it will be LambdaTimestreamFullAccessRole.
  8. Click Create function.

Configure the AWS Lambda Function

  1. Open the AWS management console for AWS Lambda.
  2. Select the newly-created function, in this example it will be PrometheusConnector.
  3. Under the code section, click Upload from.
  4. Select .zip file.
  5. Upload the ZIP file containing the precompiled Linux binary, which can be downloaded from the latest releases.
  6. Click save.
  7. Click on Configuration.
  8. Under the Environment variables section, click Edit.
  9. Enter the key and corresponding value of environment variables default_database, default_table, which are required, and region will default to us-east-1 if not populated. Here is an example: Go to Configuration Options to see more information.
  10. Click on Save
  11. Click on Code.
  12. Click Edit in the Runtime settings section.
  13. In the Handler section, enter the name of the Amazon Timestream Prometheus Connector ZIP file, which will be bootstrap.
  14. Click Save.

Create the API on Amazon API Gateway

  1. Open the AWS management console for API Gateway.
  2. Click Create API.
  3. Click Build under HTTP API.
  4. Click Add integration and choose Lambda.
  5. Choose the appropriate AWS Region.
  6. Type in the name of the Lambda function created in the previous step Create the AWS Lambda Function or paste in the function's ARN. For this example, the value will be PrometheusConnector.
  7. Click Add integration.
  8. Type in an API name, this example will use PrometheusConnectorAPI as the API name.
  9. Click Next.
  10. There will be a pre-defined route ANY /PrometheusConnector -> PrometheusConnector(Lambda), remove this route.
  11. Click Add route to add a new route for remote write.
  12. Choose POST for the Method.
  13. Enter /writefor the Resource Path.
  14. Select the appropriate Lambda function as the integration target, in this example it would be PrometheusConnector.
  15. Click Add route to add a new route for remote read.
  16. Choose POST for the Method.
  17. Enter /read for the Resource Path.
  18. Select the appropriate Lambda function as the integration target, in this example it would be PrometheusConnector.
  19. Click Next.
  20. Remove the default stage.
  21. Click Add stage.
  22. Enter a name for the stage, in this example we will use dev.
  23. Click the Auto-deploy button and click Next.
  24. Click Create.
  25. Select the newly-created API Gateway and take note of the invoke URL, this URL is required to set up Prometheus' remote read and write URL.
  26. It is highly recommended having TLS encryption enabled during production. See Configuring mutual TLS authentication for an HTTP API.

Configure Prometheus for AWS Lambda

The process to configure Prometheus for AWS Lambda requires the same steps listed in Prometheus Configuration.

NOTE: Ensure the remote write and the remote read URLs are set to the invoke URLS.

The following example points the remote write and the remote read URLs to an API with ID foo9l30 and the deployment stage dev. It is highly recommended having TLS encryption enabled during production, the following example also specifies the root certificate authority file for TLS encryption, which requires configuring the TLS encryption on API Gateway. See Configuring mutual TLS authentication for an HTTP API.

remote_write:
  - url: "https://foo9l30.execute-api.us-east-1.amazonaws.com/dev/write"
      
    tls_config:
      # Ensure ca_file is a valid file path pointing to the CA certificate.
      ca_file: RootCA.pem

remote_read:
  - url: "https://foo9l30.execute-api.us-east-1.amazonaws.com/dev/read"
  
    tls_config:
      # Ensure ca_file is a valid file path pointing to the CA certificate.
      ca_file: RootCA.pem

Configure Logging for API Gateway (Optional)

To configure logging for API Gateway to CloudWatch, a new CloudWatch log group needs to be created.

  1. Open the AWS management console for CloudWatch.
  2. Click the Log groups under Log in the sidebar.
  3. Select Create log group.
  4. Enter a group name and click Create.
  5. Select the newly-created log group from the list of log groups to see the log group details.
  6. Take note of the ARN.

Next, open the previously created Prometheus Connector API on API Gateway to configure logging.

  1. Select Logging under the Monitor section on the left-hand side.
  2. Select a stage from the dropdown and click Next.
  3. Click Edit.
  4. Toggle Access logging.
  5. Paste the ARN of the newly-created log group.
  6. Select the preferred log format.
  7. Click Save.

If permissions are required, add the following policy to the IAM account with region and account-id updated to the appropriate values:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "logs:DescribeLogGroups",
                "logs:DescribeLogStreams",
                "logs:GetLogEvents",
                "logs:FilterLogEvents"
            ],
            "Resource": "arn:aws:logs:region:account-id:log-group:*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "logs:CreateLogDelivery",
                "logs:PutResourcePolicy",
                "logs:UpdateLogDelivery",
                "logs:DeleteLogDelivery",
                "logs:CreateLogGroup",
                "logs:DescribeResourcePolicies",
                "logs:GetLogDelivery",
                "logs:ListLogDeliveries"
            ],
            "Resource": "*"
        }
    ]
}

For more information see Configuring logging for an HTTP API.

View Logs on CloudWatch

A log group will automatically be created when creating a new AWS Lambda function. The name of log group is usually in the following format: the log group named in format /aws/lambda/{LambdaFunctionName}. To view the logs from the Prometheus Connector:

  1. Open the AWS management console for CloudWatch.
  2. Click Log groups under Logs.
  3. Select the log group for the Prometheus Connector. In this example it would be /aws/lambda/PrometheusConnector and here is an example of the log streams within:

Start Prometheus

Start Prometheus by running the command: ./prometheus --config.file=prometheus.yml.

Configuration Options

NOTE: All configuration options keys are case-sensitive

When running the Prometheus Connector on AWS Lambda, configuration options need to be set as Lambda environment variables.

Standard Configuration Options

The default-database name and default-table name are required for data ingestion and data retrieval. If they are not provided, the Prometheus Connector will return a 400 Bad Request to the caller.

Standalone Option Lambda Option Description Required Default Value
default-database default_database The Prometheus default database name. No None
default-table default_table The Prometheus default table name. No None
region region The signing region for the Amazon Timestream service. No us-east-1
tls-certificate N/A The path to the TLS server certificate file. This is required to enable HTTPS. If unspecified, HTTP will be used. No None
tls-key N/A The path to the TLS server private key file. This is required to enable HTTPS. If unspecified, HTTP will be used. No None
web.listen-address N/A The endpoint to listen to for write and read requests sent from Prometheus. No :9201
web.telemetry-path N/A The path containing metrics collected by the Prometheus Connector, such as ignoredSamples. This allows Prometheus to scrape and monitor data from the specified telemetry-path. No /metrics

NOTE: web.listen-address and web.telemetry-path configuration options are not available when running the Prometheus Connector on AWS Lambda.

Configuration Examples

  1. Configure the Prometheus Connector to access the Amazon Timestream service in the US West (Oregon) Region instead of the default US East (N. Virginia) Region.

    Runtime Command
    Precompiled Binaries ./bootstrap --default-database=PrometheusDatabase --default-table=PrometheusMetricsTable --region=us-west-2
    AWS Lambda Function aws lambda update-function-configuration --function-name PrometheusConnector --environment "Variables={default_database=prometheusDatabase,default_table=prometheusMetricsTable,region=us-west-2}"

  2. Configure the Prometheus Connector listen for requests on an HTTPS server https://localhost:9201 with TLS encryption.

    Runtime Command
    Precompiled Binaries ./bootstrap --default-database=PrometheusDatabase --default-table=PrometheusMetricsTable --tls-certificate=serverCertificate.crt --tls-key=serverPrivateKey.key
    AWS Lambda Function N/A

  3. Configure the Prometheus Connector to listen for Prometheus requests on http://localhost:3080.

    Runtime Command
    Precompiled Binaries ./bootstrap --default-database=PrometheusDatabase --default-table=PrometheusMetricsTable --web.listen-address=:3080
    AWS Lambda Function N/A

  4. Configure the Prometheus Connector to listen for Prometheus requests on http://localhost:3080 and serve collected metrics to http://localhost:3080/timestream-metrics.

    Runtime Command
    Precompiled Binaries ./bootstrap --default-database=PrometheusDatabase --default-table=PrometheusMetricsTable --web.listen-address=:3080 --web.telemetry-path=/timestream-metrics
    AWS Lambda Function N/A

Retry Configuration Options

The Prometheus Connector exposes the query SDK's retry configurations for users.

Standalone OptionOption Lambda Option Description Is Required Default Value
max-retries max_retries The maximum number of times the read request will be retried for failures. No 3

Configuration Examples

Configure the Prometheus Connector to retry up to 10 times upon recoverable errors.

Runtime Command
Precompiled Binaries ./bootstrap --default-database=PrometheusDatabase --default-table=PrometheusMetricsTable --max-retries=10
AWS Lambda Function aws lambda update-function-configuration --function-name PrometheusConnector --environment "Variables={default_database=prometheusDatabase,default_table=prometheusMetricsTable,max_retries=10}"

Logger Configuration Options

Standalone Option Lambda Option Description Required Default Value Valid Values
enable-logging enable_logging Enables or disables logging in the Prometheus Connector. No true 1, t, T, TRUE, true, True, 0, f, F, FALSE, false, False
fail-on-long-label fail_on_long_label Enables or disables the option to halt the program immediately when a Prometheus Label name exceeds 256 bytes. No false 1, t, T, TRUE, true, True, 0, f, F, FALSE, false, False
fail-on-invalid-sample-value fail_on_invalid_sample_value Enables or disables the option to halt the program immediately when a Sample contains a non-finite float value. No false 1, t, T, TRUE, true, True, 0, f, F, FALSE, false, False
log.level log_level Sets the output level for logs. No info info, warn, debug, error
log.format log_format Sets the output format for the logs. The output for logs always goes to stderr, unless the logging has been disabled. No logfmt logfmt, json

Setting log levels:

  • SAM CLI - sam deploy --parameter-overrides "LogLevel=Debug"
  • One-click deployment - Update the log_level environment variable when configuring the deployment parameters
    • With an already deployed Lambda you can edit the environment variable log_level in the Lambda configuration
  • Local execution - ./bootstrap --default-database=PrometheusDatabase --default-table=PrometheusMetricsTable --log.level=info

NOTE: The logging level is by default set to info. Set log.level to debug to view any Samples ignored due to long metric name or non-finite values.

fail-on-long-label — Prometheus recommends using meaningful and detailed metrics names, which may result in metric names exceeding the maximum length (256 bytes) supported by Amazon Timestream. If a Prometheus time series has a metric name exceeding the maximum supported length, the Prometheus Connector will by default log and ignore the Prometheus time series. To quickly spot and resolve issues that may be caused by ignored Prometheus time series during development, set fail-on-long-label flag to true, and the Prometheus Connector will log and halt on a long metric name.

fail-on-invalid-sample-value — If the Prometheus WriteRequest contains time series with non-finite float values such as NaN, -Inf, or Inf, the Prometheus Connector will by default log and ignore any of those time series. To quickly spot and resolve issues that may be caused by ignored Prometheus time series during development, set fail-on-invalid-sample-value flag to true, and the Prometheus Connector will log and halt on a Prometheus time series with non-finite float values. fail-on-long-label and fail-on-invalid-sample-value configurations are not recommended during production operation.

Configuration Examples

  1. Disable logging in the Prometheus Connector.

    Runtime Command
    Precompiled Binaries ./bootstrap --default-database=PrometheusDatabase --default-table=PrometheusMetricsTable --enable-logging=false
    AWS Lambda Function aws lambda update-function-configuration --function-name PrometheusPrometheus Connector --environment "Variables={default_database=prometheusDatabase,default_table=prometheusMetricsTable,enable_logging=false}"

  2. Toggle the Prometheus Connector to halt on:
    - label names exceeding the maximum length supported by Amazon Timestream;
    - Prometheus time series with non-finite values.

    Runtime Command
    Precompiled Binaries ./bootstrap --default-database=PrometheusDatabase --default-table=PrometheusMetricsTable --fail-on-long-label=true --fail-on-invalid-sample=true
    AWS Lambda Function aws lambda update-function-configuration --function-name PrometheusConnector --environment "Variables={default_database=prometheusDatabase,default_table=prometheusMetricsTable,fail_on_long_label=true, fail_on_invalid_sample_value=true}"

  3. Configure the Prometheus Connector to output the logs at debug level and in JSON format.

    Runtime Command
    Precompiled Binaries ./bootstrap --default-database=PrometheusDatabase --default-table=PrometheusMetricsTable --log.level=debug --log.format=json
    AWS Lambda Function aws lambda update-function-configuration --function-name PrometheusConnector --environment "Variables={default_database=prometheusDatabase,default_table=prometheusMetricsTable,log_level=debug, log_format=json}"

Relabel Long Labels

If a Prometheus time series has a metric name exceeding the maximum supported length, the Prometheus Connector will by default log and ignore any of those Samples.

Long metric names will be logged by the Prometheus Connector, one can use the write_relabel_configs in prometheus.yml to rename a long metric name. Below is an example prometheus.yml relabeling the long metric name prometheus_remote_storage_read_request_duration_seconds_bucket to prometheus_read_request_duration_seconds_bucket.

global:
  scrape_interval:    60s
  evaluation_interval: 60s 

scrape_configs:
  - job_name: 'prometheus'

    static_configs:
    - targets: ['localhost:9090']

remote_write:
  - url: "http://localhost:9201/write"

    write_relabel_configs:
    - source_labels: ["__name__"]
      action: replace
      regex: prometheus_remote_storage_read_request_duration_seconds_bucket
      replacement: prometheus_read_request_duration_seconds_bucket
      target_label: __name__

remote_read:
  - url: "http://localhost:9201/read"

Authentication

When the connector is deployed as a Lambda function, authentication is handled by passing through credentials with each request; validation is done within the Lambda function using the AWS SDK for Go. In general, the Timestream Prometheus Connector will use the default credentials provider implemented in the AWS SDK for Go instead of allowing users to provide the credentials through command-line flags. This prevents sensitive data from being easily scraped.

Due to Prometheus' lack of support for SigV4 (see the Unsupported SigV4 Authentication section), the API Gateway deployed via one-click deployment or with the serverless/template.yml CloudFormation template does not use SigV4 for its public endpoints.

User-Agent Header

The Prometheus Connector uses the following User-Agent header for all requests:

User-Agent: Prometheus Connector/<version> aws-sdk-go/<version> (go<version>; <os>; <cpu arch>)

Verification

  1. To verify Prometheus is running, open http://localhost:9090/ in a browser, this opens Prometheus' expression browser.

  2. To verify the Prometheus Connector is ready to receive requests, ensure the following log message is printed. See the Troubleshooting section for other error messages.

    level=info ts=2020-11-21T01:06:49.188Z caller=utils.go:33 message="Timestream <write/query> connection is initialized (Database: <database-name>, Table: <table-name>, Region: <region>)"

  3. To verify the Prometheus Connector is ingesting data, use the AWS CLI to execute the following query:

    aws timestream-query query --query-string "SELECT count() FROM prometheusDatabase.prometheusMetricsTable"

    The output should look similar to the following:

    {
    "Rows": [
        {
            "Data": [
                {
                    "ScalarValue": "340"
                }
            ]
        }
    ],
    "ColumnInfo": [
        {
            "Name": "_col0",
            "Type": {
                "ScalarType": "BIGINT"
            }
        }
    ],
    "QueryId": "AEBQEAMYNBGX7RA"
}

    This sample output indicates that 340 rows has been ingested.

  4. To verify the Prometheus Connector can query data from Amazon Timestream, visit http://localhost:9090/ in a browser, which opens Prometheus' expression browser, and execute a Prometheus Query Language (PromQL) query. The PromQL query will use the values of default-database and default-table as the corresponding database and table that contains data. Here is a simple example:

    prometheus_http_requests_total{}

    prometheus_http_requests_total is a metric name. The database and table being queried are the corresponding default-database and default-table configured for the Prometheus connector. This PromQL will return all the time series data from the past hour with the metric name prometheus_http_requests_total in default-table of default-database. Here is a query result example:

    PromQL also supports regex, here is an example:

    prometheus_http_requests_total{handler!="/api/v1/query", job=~"p*", code!~"2.."}

    This example queries all rows from prometheusMetricsTable of prometheusDatabase where:

    • column metric name equals to prometheus_http_requests_total;
    • column handler does not equal to /api/v1/query;
    • column job matches the regex pattern p*;
    • column code does not match the regex pattern 2...

    For more examples, see Prometheus Query Examples. There are other ways to execute PromQLs, such as through Prometheus' HTTP API, or through Grafana.

Developer Documentation

Building the Prometheus Connector from Source

  1. Ensure to download all the dependencies, run the command: go get -u -v -f all.
  2. Use the command to build the program: go build.
  3. Now, proceed from the Prometheus Configuration section in User Documentation to run the connector.

Building the Docker Image

  1. Navigate to the repository’s root directory on a command-line interface.
  2. Run the following command to build the image: docker buildx build . -t timestream-prometheus-connector-docker.

Troubleshooting

Information Logs

The following logs can be useful for determining if any records are ignored by the Prometheus Connector. Enable debug logging if you require additional information about rejected records.

Records ignored by the Prometheus Connector:

x number of records were rejected for ingestion to Timestream. See Troubleshooting in the README for why these may be rejected, or turn on debug logging for additional info.

Records requested for ingestion through the Prometheus Connector:

x records requested for ingestion from Prometheus.

Records successfully ingested through the Prometheus Connector:

Successfully wrote x records to database: PrometheusDatabase table: PrometheusMetricsTable

Prometheus Connector Specific Errors

Note: Errors and records are only logged with debug mode. With the default log level of info, only the high level errors are logged. See Logger Configuration Options for how to adjust the logging level.

All connector-specific errors can be found in errors/errors.go.

  1. Error: LongLabelNameError

    Description: The metric name exceeds the maximum supported length and the fail-on-long-label is set to true.

    Log Example

    level=error ts=2020-11-06T02:01:46.753Z calleawr=utils.go:23 message="Unable to convert the received Prometheus write request to Timestream Records."
error="LongLabelNameError: metric name 'prometheus_remote_storage_read_request_duration_seconds_bucket' exceeds 60 characters, the maximum length supported by Timestream"

    Solution

    1. Rename the invalid metric name using the relabelling method in Relabel Long Labels section.
    2. Set the fail-on-long-label to false, which means the Prometheus Connector will log and not attempt to ingest the time series containing the long metric name.

  2. Error: InvalidSampleValueError

    Description: The Prometheus WriteRequest contains time series with unsupported non-finite float Sample values such as NaN, -Inf, or Inf and the fail-on-invalid-sample-value is set to true.

    Log Example

    debug ts=2020-11-06T02:29:26.760Z caller=utils.go:28 message="Timestream only accepts finite IEEE Standard 754 floating point precision. Samples with NaN, Inf and -Inf are ignored." 
timeSeries="labels:<name:\"__name__\" value:\"prometheus_rule_evaluation_duration_seconds\" > labels:<name:\"instance\" value:\"localhost:9090\" > labels:<name:\"job\" value:\"prometheus\" > 
labels:<name:\"monitor\" value:\"codelab-monitor\" > labels:<name:\"quantile\" value:\"0.99\" > labels:<name:\"prometheusDatabase\" value:\"promDB\" > labels:<name:\"prometheusMetricsTable\" value:\"prom\" >
samples:<value:nan timestamp:1604629766606 > "

    Solution

    Users can set the fail-on-invalid-sample-value to false, and the Prometheus Connector will log and not attempt to ingest any Prometheus time series with non-finite Sample value. For more details, see the Log Configuration Options.

  3. Error: MissingDatabaseWithWriteError

    Description: The default database environment variable has not been set.

    Log Example

    error="InvalidDestinationError: the given database name: timestreamDatabase cannot be found for the current time series labels:<name:\"__name__\"
value:\"go_gc_duration_seconds\" > labels:<name:\"instance\" value:\"localhost:9090\" > labels:<name:\"job\" value:\"prometheus\" > labels:<name:\"monitor\" value:\"codelab-monitor\" >
 labels:<name:\"quantile\" value:\"0\" > labels:<name:\"prometheusDatabase\" value:\"promDB\" > labels:<name:\"prometheusMetricsTable\" value:\"prom\" > samples:<timestamp:1604627351607 > "

    Solution

    1. Ensure default-database and default-table are set when running Prometheus Connector. Note that the configuration options for the AWS Lambda integration are in snake_case. For more details and examples see the Advanced Options section.

  4. Error: NewMissingTableWithWriteError

    Description: The default table is not configured with the environment variable default-table.

    Log Example

    level=error ts=2020-11-07T01:47:30.752Z caller=utils.go:23 message="Unable to convert the received Prometheus write request to Timestream Records." error="The given table name:
timestreamTableName cannot be found for the current time series:<name:\"__name__\" value:\"prometheus_tsdb_tombstone_cleanup_seconds_bucket\" > 
labels:<name:\"instance\" value:\"localhost:9090\" > labels:<name:\"job\" value:\"prometheus\" > labels:<name:\"le\" value:\"0.005\" > labels:<name:\"monitor\" value:\"codelab-monitor\" > 
labels:<name:\"prometheusDatabase\" value:\"promDB\" > labels:<name:\"prometheusMetricsTable\" value:\"prom\" > samples:<timestamp:1604713406607 > "

    Solution

    1. Ensure default-database and default-table are set when running Prometheus Connector. Note that the configuration options for the AWS Lambda integration are in snake_case. For more details and examples see the Advanced Options section.

  5. Error: NewMissingDatabaseError

    Description: The environment variable default-database must be specified for the Prometheus Connector.

    Log Example

    level=error ts=2020-11-07T01:49:31.041Z caller=utils.go:23 message="Error occurred while reading the data back from Timestream." error="the given database name: <exampledatabase> cannot be found. Please provide the table name with the flag default-database."

    Solution

    Set the environment variable default-database to the destination database for the Prometheus Connector.

  6. Error: NewMissingTableError

    Description: The environment variable default-table must be specified for the Prometheus Connector.

    Log Example

    level=error ts=2020-11-07T01:48:53.694Z caller=utils.go:23 message="Error occurred while reading the data back from Timestream." error="the given table name: <tablename> cannot be found. Please provide the table name with the flag default-table"

    Solution

    Set the environment variable default-table to the destination table for the Prometheus Connector.

  7. Error: MissingDestinationError

    Description: The environment variables default-database and default-table must be specified for the Lambda function.

    Solution

    Set the environment variables default-database and default-table for the AWS Lambda Function with the following command, update the function name if necessary:

    aws lambda update-function-configuration --function-name PrometheusConnector --environment "Variables={default_database=prometheusDatabase,default_table=prometheusMetricsTable}"

    For more information, please go to Configure the AWS Lambda Function.

  8. Error: ParseEnableLoggingError

    Description: The value set for the enable-logging option is not an accepted value.

    Solution

    Check the accepted list of values for enable-logging in the Logger Configuration Options section.

  9. Error: ParseMetricLabelError

    Description: The value set for the fail-on-long-label option is not an accepted value.

    Solution

    Check the accepted list of values for fail-on-long-label in the Logger Configuration Options section.

  10. Error: ParseSampleOptionError

    Description: The value set for the fail-on-invalid-sample-value option is not an accepted value.

    Solution

    Check the accepted list of values for fail-on-invalid-sample-value in the Logger Configuration Options section.

  11. Error: MissingHeaderError

    Description: This error may occur when running the Prometheus Connector on AWS Lambda. The request sent to the Prometheus Connector is missing either the x-prometheus-remote-read-version or the x-prometheus-remote-write-version header.

    Solution

    Check the request headers and add x-prometheus-remote-read-version or x-prometheus-remote-write-version to the request headers. This error returns a 400 Bad Request status code to the caller.

  12. Error: ParseRetriesError

    Description: This error will occur when the max-retries option has an invalid value.

    Solution

    See the Retry Configuration Options section for acceptable formats for the max-retries option.

  13. Error: UnknownMatcherError

    Description: This error will occur when an unknown matcher is within a PromQL query. Prometheus only supports four types of matchers within a filter: =, !=, =~, and !~.

    Solution

    Re-evaluate your PromQL query and ensure you are using only the above matchers.

Write API Errors

Errors Status Code Description Solution
ValidationException 400 Invalid or malformed request. Please check if the provided default-database or default-table values are set and review the Configuration Options.
ServiceQuotaExceededException 402 Instance quota of resource exceeded for this account. Remove unused instance or upgrade the total number of resources for this account.
AccessDeniedException 403 You are not authorized to perform this action Ensure you have sufficient access to Amazon Timestream.
ResourceNotFoundException 404 The operation tried to access a non-existent resource. Specify the resource correctly, or check whether its status is not ACTIVE.
ConflictException 409 Amazon Timestream was unable to process this request because it contains a resource that already exists. Update the request with correct resource.
RejectedRecordsException 419 Amazon Timestream will throw this exception in the following cases:
1. Records with duplicate data where there are multiple records with the same dimensions, timestamps, and measure names but different measure values.
2. Records with timestamps that lie outside the retention duration of the memory store.
3. Records with dimensions or measures that exceed the Amazon Timestream defined limits.		 1. Check and process the data to ensure that there are no different measure values at the same timestamp given other labels/filters are the same.
2. Check or update the retention duration in database.
InvalidEndpointException 421 The requested endpoint was invalid. Check whether the endpoint is NIL or in an incorrect format.
ThrottlingException 429 Too many requests were made by a user exceeding service quotas. The request was throttled. Continue to send data at the same (or higher) throughput. Go to Data Ingestion for more information.
InternalServerException 500 Amazon Timestream was unable to fully process this request because of an internal server error. Please send the request again later.

Query API Errors

Errors Status Code Description Solution
QueryExecutionException 400 Amazon Timestream was unable to run the query successfully. See the logs to get more information about the failed query.
ValidationException 400 Invalid or malformed request. Check whether the query contains invalid regex (see Unsupported RE2 Syntax) or an invalid matcher in the query.
AccessDeniedException 403 You are not authorized to perform this action Ensure you have sufficient access to Amazon Timestream.
ConflictException 409 Unable to poll results for a cancelled query. The query is cancelled. Please resend.
InvalidEndpointException 421 The requested endpoint was invalid Check whether the endpoint is NULL or in an incorrect format.
ThrottlingException 429 The request was denied due to request throttling. Continue to send queries at the same (or higher) throughput.
InternalServerException 500 Amazon Timestream was unable to fully process this request because of an internal server error. Please send the request again later.

Caveats

Unsupported SigV4 Authentication

Prometheus supports SigV4 for the remote_write protocol with limitations and lacks SigV4 support for the remote_read protocol. With the deployment method of the Prometheus Connector being a lambda function, the service portion of the SigV4 header must be set to the value execute-api. Prometheus hard-codes this value to aps, limiting SigV4 support to Amazon Managed Service for Prometheus. Integrating SigV4 support will require remote_read SigV4 support added and configuration settings for the service portion of the SigV4 header integrated with Prometheus.

If SigV4 is required, SigV4 authentication is possible by running Prometheus with a sidecar. This will require enabling IAM authentication for the APIGateway deployment, which is not covered in the Prometheus Connector documentation.

Unsupported Temporary Security Credentials

All Prometheus requests sent to the Prometheus Connector will be authorized through the AWS SDK for Go. The Prometheus Connector only supports passing the IAM user access key and the IAM user secret access key through the basic authentication header.

It is recommended to regularly rotate IAM user access keys.

Unsupported RE2 Syntax

Prometheus follows the RE2 syntax https://github.com/google/re2/wiki/Syntax, while Amazon Timestream supports the Java regex pattern. Any query with unsupported regex syntax will result in a 400 Bad Request status code.

Unsupported RE2 Regex Functionality Sample PromQL
(?P<name>\w+) Named and numbered capturing group up{job=~"(?P<name>\\w+)"}

Inaccurate Prometheus Metrics

Prometheus tracks all time series successfully sent to the remote write storage and all rejected Prometheus time series. Since the Prometheus Connector does not ingest Prometheus time series with non-finite float sample values or time series with long metric names exceeding the supported length limit, the Prometheus' metrics for successful time series are inaccurate. To find out the number of ignored Prometheus time series and the total number of Prometheus time series received, check the metrics at <web.listen-address><web.telemetry-path>, for instance, http://localhost:9201/metrics.

License

This library is licensed under the Apache 2.0 License.

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