AWS Timestream Integration
Monitor write and query metrics, storage utilization, and magnetic store transitions for your Timestream time-series database. Get AI-powered write anomaly detection and query cost analysis to keep ingestion pipelines healthy.
How It Works
Create IAM Role for Metric Streams
Provision an IAM role with CloudWatch permissions scoped to the AWS/Timestream namespace. TigerOps uses this role to deliver Timestream database and table metrics via Firehose.
Deploy CloudWatch Metric Streams
Run the TigerOps CloudFormation stack to stream the AWS/Timestream namespace. Write record rates, query execution times, and storage metrics begin flowing immediately.
Configure Table-Level Monitoring
Tag your Timestream databases and tables by application and data tier. TigerOps uses these tags to group metrics per application and identify tables with anomalous write or query patterns.
Set Write Rejection Alerts
Configure alerts on SystemErrors, InvalidRequests, and SuccessfulRequestLatency. TigerOps fires alerts when write failures spike and correlates them with ingestion pipeline events.
What You Get Out of the Box
Write Metrics
Records written per second, write request latency, system errors, invalid requests, and throttled requests per Timestream table. Identify write bottlenecks and schema validation failures.
Query Performance
Query execution time, bytes scanned, data points returned, and query failures per database. TigerOps alerts when expensive queries scan excessive data and drive up costs.
Storage Utilization
Memory store and magnetic store data usage per table with historical growth trending. Predict when data retention policy adjustments are needed before storage costs spike.
Magnetic Store Transitions
Track the rate of data being moved from memory store to magnetic store. Measure transition latency and data rejection rates when magnetic store writes are delayed.
Ingestion Pipeline Monitoring
End-to-end ingestion latency from source event time to Timestream write confirmation. Detect late-arriving data that falls outside the memory store retention window.
AI Write Anomaly Detection
TigerOps establishes per-table write rate baselines and fires alerts when ingestion rates drop or spike anomalously, catching upstream pipeline failures before data gaps form.
CloudFormation Stack for Timestream Metric Streams
Deploy the TigerOps CloudFormation stack to stream Timestream database and table metrics in minutes.
# TigerOps CloudFormation — Timestream Metric Streams
# aws cloudformation deploy \
# --template-file tigerops-timestream-streams.yaml \
# --stack-name tigerops-timestream \
# --capabilities CAPABILITY_IAM
Parameters:
TigerOpsApiKey:
Type: String
NoEcho: true
Resources:
TigerOpsTimestreamStream:
Type: AWS::CloudWatch::MetricStream
Properties:
Name: tigerops-timestream-stream
FirehoseArn: !GetAtt TigerOpsDeliveryStream.Arn
RoleArn: !GetAtt MetricStreamRole.Arn
OutputFormat: opentelemetry0.7
IncludeFilters:
- Namespace: AWS/Timestream
- Namespace: AWS/TimestreamInfluxDB
StatisticsConfigurations:
- AdditionalStatistics:
- p50
- p90
- p99
IncludeMetrics:
- Namespace: AWS/Timestream
MetricName: SuccessfulRequestLatency
TigerOpsDeliveryStream:
Type: AWS::KinesisFirehose::DeliveryStream
Properties:
HttpEndpointDestinationConfiguration:
EndpointConfiguration:
Url: https://ingest.atatus.net/api/v1/cloudwatch
AccessKey: !Ref TigerOpsApiKey
RequestConfiguration:
CommonAttributes:
- AttributeName: service
AttributeValue: timestream
RetryOptions:
DurationInSeconds: 60Common Questions
Which Timestream metrics does TigerOps collect?
TigerOps collects all AWS/Timestream CloudWatch metrics including SuccessfulRequestLatency, SystemErrors, InvalidRequests, ThrottledRequests, UserErrors, DataPointsIngested, and storage utilization metrics per database and table dimensions.
Can TigerOps monitor Timestream for InfluxDB?
Yes. AWS Timestream for InfluxDB publishes metrics to its own CloudWatch namespace. TigerOps supports both Timestream for LiveAnalytics and Timestream for InfluxDB with separate Metric Stream filters and dashboards for each engine type.
How does TigerOps help control Timestream query costs?
TigerOps tracks bytes scanned per query over time and alerts when specific dashboards or applications are issuing queries that scan significantly more data than their historical baseline. This allows teams to optimize queries before they cause cost overruns.
Does TigerOps alert when data falls outside the memory store retention window?
Yes. TigerOps monitors the timestamp distribution of incoming writes. When write timestamps lag behind real-time by more than the memory store retention period, TigerOps fires an alert indicating that data will be rejected or written to magnetic store at higher latency.
Can I use TigerOps to monitor Timestream as a metrics backend for other services?
Yes. Many teams use Timestream as the backend for IoT and observability data. TigerOps can simultaneously monitor Timestream performance and the upstream services writing to it, giving a complete data pipeline view in one dashboard.
Stop Silent Timestream Ingestion Failures From Creating Data Gaps
Write metrics, magnetic store monitoring, and AI ingestion anomaly detection. Deploy in 5 minutes.