- Throughput Metrics: Monitor
ConsumedReadCapacityUnitsandConsumedWriteCapacityUnitsto track usage and avoid throttling. Compare withProvisionedReadCapacityUnitsandProvisionedWriteCapacityUnitsto identify over- or under-provisioning. - Latency Metrics: Keep an eye on
SuccessfulRequestLatencyto spot performance issues. Monitor percentiles like p99 for spikes and latency trends. - Error Metrics: Watch
SystemErrors(internal issues) andUserErrors(client-side mistakes) to identify and resolve problems quickly. - Storage Metrics: Use
TableSizeBytesandItemCountto track data growth and plan capacity. MonitorTimeToLiveDeletedItemCountto ensure TTL deletions work as expected. - Throttling and Conflicts: Track
ThrottledRequeststo prevent capacity limits andTransactionConflictto resolve contention in concurrent transactions.
Why it matters:
CloudWatch provides real-time and historical insights into DynamoDB’s performance. Use these metrics to set alarms, auto-scale, and control costs. For example, configure alerts at 80% of capacity to avoid throttling, or switch to on-demand mode if utilization is consistently low. Monitoring these metrics ensures smooth operations and better cost efficiency.
Want to dive deeper? Read on for actionable tips on fine-tuning DynamoDB with CloudWatch.
Throughput Metrics
Throughput metrics are key to understanding how well your DynamoDB tables manage read and write operations. By comparing your provisioned capacity to actual usage, these metrics guide you in making smarter decisions about capacity management and cost efficiency.
ConsumedReadCapacityUnits and ConsumedWriteCapacityUnits
ConsumedReadCapacityUnits and ConsumedWriteCapacityUnits measure how much of your provisioned throughput is being used over a specific time frame. These metrics apply to both provisioned and on-demand capacity modes.
DynamoDB tracks throughput in one-minute intervals. To determine the average consumption per second, simply divide the Sum value for one minute by 60.
Keeping an eye on these metrics helps you spot unusual spikes or dips in read and write activities. For instance, if your application usually consumes 100 read capacity units per minute but suddenly jumps to 500, it might signal increased user activity, a change in data access patterns, or even issues in your application logic.
Set up monitoring to trigger alerts before throttling occurs. A common practice is to configure alerts when usage reaches around 80% of your provisioned capacity, giving you enough time to make adjustments.
After checking consumed capacity, evaluate your provisioned capacity to ensure you're operating efficiently.
ProvisionedReadCapacityUnits and ProvisionedWriteCapacityUnits
ProvisionedReadCapacityUnits represent the read capacity allocated for your table or global secondary index, while ProvisionedWriteCapacityUnits cover write operations. In provisioned mode, you pay for this capacity regardless of actual usage.
Comparing provisioned capacity to consumed capacity can reveal inefficiencies. If consumed capacity is consistently much lower than provisioned capacity, you're likely over-provisioned and overspending. For example, nOps defines a table as "underutilized" if consumed capacity is at least 30% below provisioned capacity.
On the flip side, if consumed capacity regularly exceeds 80% of provisioned capacity, your table might be under-provisioned and at risk of throttling. DynamoDB's auto-scaling feature kicks in when consumed capacity exceeds the target utilization for two consecutive minutes, and scales down when 15 consecutive data points fall below the target.
Cost efficiency is another factor to consider. If your workload consistently uses more than 18% of your provisioned capacity, the provisioned capacity mode is usually more cost-effective than on-demand mode. This helps you choose the best capacity mode for your needs.
By identifying mismatches between provisioned and consumed capacity, you can better anticipate and prevent throttling.
ThrottledRequests
The ThrottledRequests metric tracks the number of user requests that include at least one event exceeding your provisioned throughput. This is a clear indicator that your table has hit its capacity limits.
For deeper insights, compare ThrottledRequests with ReadThrottleEvents and WriteThrottleEvents to pinpoint whether read or write operations are causing the bottleneck.
Latency and Performance Metrics
Latency metrics are essential for understanding how swiftly DynamoDB responds to requests, helping you spot performance hiccups before they impact users. These metrics focus on DynamoDB's internal operation times and can shed light on conflicts that might slow down your application. They work hand-in-hand with throughput metrics, offering a clearer view of potential bottlenecks and transactional delays.
SuccessfulRequestLatency
SuccessfulRequestLatency measures the time it takes for requests to complete, excluding any delays from network or client-side overhead. Thanks to DynamoDB's multi-AZ replication, single operations typically achieve latencies in the single-digit millisecond range.
To identify performance issues, it's crucial to understand latency percentiles. The median (p50) and average latency will give you a baseline for normal operations, while higher percentiles like p99 often reflect occasional spikes. However, if the median or average latency shows a consistent increase, it's a sign that further investigation is needed. For applications with strict latency requirements, monitoring p99.9 can help detect issues affecting a small number of requests. If you notice persistent latency increases, check the AWS Service Health Dashboard or Personal Health Dashboard. Additionally, log request IDs for slow operations when reaching out to AWS Support. The metric's dimensions - TableName, Operation, and StreamLabel - allow you to dive deeper, identifying whether latency problems are widespread or tied to specific query patterns.
TransactionConflict
While latency metrics focus on response times, TransactionConflict metrics are all about identifying issues caused by concurrent modifications. This metric tracks item-level requests that are rejected due to conflicts when multiple transactions try to modify the same items simultaneously. For applications relying on DynamoDB transactions, a rise in TransactionConflict values often signals contention.
DynamoDB uses a two-phase commit process for distributed transactions, which doubles capacity consumption. When conflicts occur, serializable isolation cancels conflicting transactions to safeguard data integrity. This not only increases latency but also reduces throughput. To mitigate these issues, consider optimizing your data model to minimize contention and use exponential backoff retries. If you're working with the AWS SDK for Java, the CancellationReasons property can help you pinpoint the causes of conflicts. Keeping transaction durations short is another way to improve throughput. Monitoring this metric, alongside your application's retry behavior, can provide valuable insights into the impact of conflicts and guide you in refining your transaction strategy.
Error Metrics and System Health Monitoring
While throughput and latency metrics help track performance trends, error metrics provide real-time insights into issues that might disrupt your system. These metrics are crucial for identifying whether problems stem from DynamoDB's internal systems or your application logic, ensuring you can address them before they impact users.
SystemErrors and UserErrors
SystemErrors monitor internal service issues within DynamoDB, marked by HTTP 500 status codes. These errors signal problems originating from DynamoDB itself and should ideally stay at zero. If you notice any, use exponential backoff retries and check the AWS status page for updates.
On the other hand, UserErrors capture client-side mistakes, which return HTTP 400 status codes. These errors often result from invalid parameters, incorrect request signatures, or improper query formatting. To troubleshoot:
- Review recent code changes affecting your queries.
- Ensure the table or index exists and is referenced correctly.
- Verify that queries are properly formatted.
- If you're using reserved words, include the expression attribute name parameter.
Both SystemErrors and UserErrors are collected by CloudWatch at one-minute intervals. To stay ahead of potential issues, set up CloudWatch alarms to alert you when these metrics deviate from zero.
ConditionalCheckFailedRequests
The ConditionalCheckFailedRequests metric tracks errors in conditional write operations, which can directly affect data consistency. This metric increments when a logical condition in operations like PutItem, UpdateItem, or DeleteItem evaluates to false, resulting in an HTTP 400 error. However, it's important to note that this metric is separate from UserErrors and doesn't include ProvisionedThroughputExceededException or ConditionalCheckFailedException errors .
Aggregated every minute, this metric is a key indicator of issues with conditional writes. Frequent failures often point to problems with your application's logic or consistency requirements. To monitor this, set up CloudWatch alarms for each DynamoDB table. Configure the alarms to trigger when the sum of ConditionalCheckFailedRequests exceeds a defined threshold - commonly set at 100 within a specific period - and ensure notifications are sent to an SNS topic .
Here’s an example CloudFormation snippet to create such an alarm:
DynamoDBTableConditionCheckAlarm:
Type: 'AWS::CloudWatch::Alarm'
Properties:
AlarmName: 'DynamoDBTableConditionCheckWritelarm'
AlarmDescription: 'Alarm when condition check errors are too high'
AlarmActions:
- !Ref DynamoDBMonitoringSNSTopic
Namespace: 'AWS/DynamoDB'
MetricName: 'ConditionalCheckFailedRequests'
Statistic: 'Sum'
Unit: 'Count'
Threshold: 100
ComparisonOperator: 'GreaterThanThreshold'
Period: 60
EvaluationPeriods: 2
If these alarms frequently trigger, it's a sign to revisit your application logic and assess your data consistency requirements. Addressing these issues promptly can help maintain the reliability of your DynamoDB operations.
Storage Metrics and Data Management
Keep tabs on data growth and control storage expenses in DynamoDB by leveraging storage metrics available through CloudWatch. These metrics provide insights into usage trends, helping you make smarter storage management decisions.
TableSizeBytes
The TableSizeBytes metric measures the total storage used by your DynamoDB table and its secondary indexes, reported in bytes. This is a key metric for keeping an eye on storage growth and planning your capacity needs. DynamoDB automatically updates this data roughly every six hours.
By tracking TableSizeBytes, you can spot trends in data growth, changes in average item sizes, and shifts in storage costs. This data can also help identify tables that might benefit from DynamoDB Standard-IA. For example, setting up CloudWatch alarms to alert you when storage exceeds a specific threshold can prompt timely adjustments.
It’s worth noting that custom metrics in CloudWatch come with a cost - about $0.30 per month in the US East (N. Virginia) Region. Be sure to factor this into your monitoring budget. Next, let’s look at the ItemCount metric to analyze item-level growth and capacity needs.
ItemCount
DynamoDB also provides the ItemCount metric, which tracks the total number of items in your table or global secondary index. This metric is updated about every six hours and can reveal patterns in data usage and growth. Historical ItemCount data is especially useful for capacity planning.
Unlike performing live item counts through full table scans (which consume read units), ItemCount is included as part of your table’s metadata at no extra cost. This makes it a practical and budget-friendly option for monitoring data volume.
Insights from ItemCount can guide you in scaling decisions and adjusting your capacity plans as your data grows.
TimeToLiveDeletedItemCount
The TimeToLiveDeletedItemCount metric, updated every minute, tracks the number of items deleted via TTL (Time to Live). TTL is a feature that automatically removes expired data, helping you reduce storage costs and improve application performance.
Monitoring this metric ensures your TTL configuration is working as intended and gives you a sense of how quickly expired data is being cleared. However, keep in mind that TTL deletions can take up to 48 hours after expiration to fully process, so there might be delays in metric updates.
A popular strategy is archiving TTL-deleted items to S3, which allows you to offload less frequently accessed data while keeping your DynamoDB table focused on current information. Additionally, if DynamoDB Streams are enabled, every TTL-deleted item is logged there, giving you the chance to capture and archive the data before it’s permanently removed.
Regularly monitor this metric to confirm your TTL setup is functioning as expected and to optimize your storage management.
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Best Practices for Using CloudWatch Metrics with DynamoDB
When managing DynamoDB, leveraging CloudWatch metrics effectively can make a huge difference in ensuring smooth performance and cost efficiency. By combining strategic monitoring with proactive adjustments, you can align your database's performance with actual usage patterns.
Creating Custom Alarms
Custom alarms in CloudWatch can help you catch performance issues in DynamoDB before they escalate. For instance, you can set alarms for ConsumedReadCapacityUnits and ConsumedWriteCapacityUnits at 80% of your provisioned capacity. This gives you enough time to scale up and prevent capacity limits from affecting users. These alarms are designed to trigger only after sustained changes, so brief spikes won’t lead to false alerts.
To avoid excessive notifications, configure throttling alarms thoughtfully. Use historical data to identify normal throttling levels and set thresholds above these baselines instead of triggering alerts for every non-zero ThrottledRequests value.
Error monitoring is another critical area. Keep an eye on both SystemErrors (indicating internal DynamoDB issues that need immediate attention) and UserErrors (caused by application-level problems like invalid queries or permission issues).
For alarm actions, you have options like sending notifications via SNS (email or SMS) or triggering Lambda functions for automated responses. When setting up alarms, always include all required dimensions; missing dimensions prevent CloudWatch from aggregating metrics properly.
These alarm setups are foundational for enabling dynamic responses, such as auto-scaling, which we'll explore next.
Auto-Scaling Based on Metrics
DynamoDB’s auto-scaling feature, powered by Application Auto Scaling, adjusts provisioned throughput dynamically based on traffic patterns. This eliminates much of the guesswork in capacity planning and ensures you're not overpaying during low-traffic periods.
To make the most of auto-scaling, define policies for your tables and indexes. Set minimum and maximum capacities along with a target utilization percentage - 70% is a common choice - to trigger timely scaling events. Scaling up happens when consumed capacity exceeds the target for two consecutive minutes, while scaling down occurs after 15 consecutive data points fall below the target. You can adjust the target utilization to values between 20% and 90%, depending on your workload.
It’s important to apply auto-scaling to both tables and global secondary indexes. This ensures balanced performance and prevents bottlenecks caused by one scaling while the other lags.
Monitor the effectiveness of your auto-scaling policies through CloudWatch metrics and tweak them as needed based on usage trends. For workloads with highly unpredictable traffic, switching to on-demand capacity mode may be a better choice.
Once auto-scaling is in place, analyzing historical metrics can further refine your approach.
Analyzing Historical Metrics for Capacity Planning
Historical metrics provide a wealth of information for fine-tuning your DynamoDB setup and managing costs effectively. Since DynamoDB retains these metrics for extended periods, you can track long-term usage trends.
Focus on ConsumedReadCapacityUnits and ConsumedWriteCapacityUnits to understand how your read and write activities have evolved. If your average provisioned capacity utilization is below 35%, on-demand mode might be a more cost-effective option. Conversely, steady or cyclical traffic patterns often favor provisioned capacity, allowing for more predictable scaling.
For provisioned capacity, use your minimum usage levels as a baseline. Committing to reserved capacity can lead to significant cost savings, especially with rolling commitments that overlap reservation periods.
Set up alerts to notify you when capacity usage reaches 80%. This gives you time to scale manually before hitting your limits. Additionally, historical data can help you identify cyclical trends, enabling you to anticipate periods of higher demand.
The CUDOS Dashboard is another valuable tool for identifying cost-saving opportunities. It provides specific recommendations for DynamoDB optimization, which you can combine with your historical metric analysis to make informed decisions.
Conclusion
Keeping an eye on CloudWatch metrics for DynamoDB is a must if you want to maintain strong performance and keep costs in check. The metrics we’ve discussed - like throughput, latency, errors, and storage - offer the insights needed to ensure your DynamoDB tables operate efficiently. Metrics such as SystemErrors and UserErrors act as early warning systems, helping you catch potential problems before they affect users.
DynamoDB automatically streams its metric data to CloudWatch, making it easy to start monitoring right away. The real trick lies in creating effective alarms and dashboards tailored to your application's performance goals.
As your workload changes, your monitoring approach should adapt too. Historical data can reveal trends that help with capacity planning. For example, if your provisioned capacity usage is consistently low, switching to on-demand mode might save you money. On the other hand, for workloads with predictable patterns, provisioned capacity paired with auto-scaling can offer better cost efficiency and performance stability.
Investing time in proper monitoring pays off by improving reliability, reducing overhead, and trimming costs. Even small applications can benefit from monitoring without breaking the bank. Start by focusing on core metrics, and as your workload grows, expand your monitoring to match. Incorporate these insights into your day-to-day operations to keep your DynamoDB tables running at their best.
FAQs
How do I use CloudWatch metrics to avoid throttling in DynamoDB?
To keep throttling at bay in DynamoDB, keep an eye on key CloudWatch metrics:
- ThrottledRequests: Monitors how often requests are being throttled.
- ConsumedReadCapacityUnits and ConsumedWriteCapacityUnits: Helps you ensure your read and write usage stays within the limits you've set.
It's a good idea to set up CloudWatch alarms to alert you when these metrics start nearing critical levels. On top of that, enabling auto-scaling can be a lifesaver. It automatically adjusts your table's capacity to match demand, reducing the risk of throttling during sudden traffic surges.
How can I use CloudWatch metrics to optimize DynamoDB costs effectively?
To make the most of DynamoDB costs using CloudWatch metrics, focus on these practical strategies:
- Keep an eye on capacity usage: Regularly track read and write capacity metrics. This ensures you're not paying for more than you need. Adjust your provisioned capacity to align with actual demand, cutting down on wasteful spending.
- Set up alerts for spikes: Use CloudWatch alarms to stay informed about sudden increases in usage. Quick action on these alerts can help you tackle potential cost issues before they escalate.
- Review throttling events: Throttling metrics can reveal capacity bottlenecks. By analyzing these, you can fine-tune your workload to prevent inefficiencies.
- Streamline indexes and access patterns: Take time to evaluate your indexes and how your data is accessed. This can help lower the costs tied to read and write operations.
For workloads that are unpredictable, switching to on-demand capacity mode can be a game-changer. It ensures you only pay for what you actually use. By regularly reviewing these metrics, you can achieve a smart balance between performance and cost management.
How can latency and transaction conflict metrics improve DynamoDB performance?
Understanding Latency and Transaction Conflict Metrics in DynamoDB
Latency metrics in DynamoDB reveal how much time it takes for requests to complete. Keeping an eye on these metrics is crucial for spotting slower operations and tweaking your setup to improve response times.
Transaction conflict metrics, on the other hand, show how frequently conflicts arise during concurrent transactions. A high rate of conflicts might suggest it's time to revisit your application's transaction patterns or adjust your partitioning strategy. This can help you increase throughput and minimize delays.
By diving into these metrics, you can fine-tune your DynamoDB configuration to enhance both performance and scalability, ensuring your applications run smoothly.
