关于Amazon Timestream查询数据扫描占比及测试异常情况的技术问询

Hi Sara, let's break down your questions about Amazon Timestream's data scan behavior step by step:

1. What does the 2% data scan represent? #

That 2% is an optimization benchmark Timestream cites for typical dashboard-style queries. These queries include specific dimensions (like device IDs or regions), measures (metrics you're aggregating), and targeted predicates (like time filters). Timestream's distributed query engine uses aggressive data pruning to discard irrelevant data blocks entirely, so only ~2% of the total data accumulated in the past six hours needs to be scanned to get the result. It's not a hard guarantee for all queries—just a representative outcome for the tool's intended monitoring use case.

2. Why reference the past six hours of data? #

Six hours is a common time window for real-time or near-real-time monitoring dashboards (one of Timestream's core use cases). Timestream stores recent "hot" data in its in-memory store, which is optimized for fast access and efficient pruning. Older "cold" data moves to a magnetic storage layer with different indexing rules. By focusing on the past six hours, the documentation highlights the best-case pruning performance when working with the most frequently accessed, optimized data tier.

3. What types of queries exhibit this behavior? #

You'll see this level of pruning with queries that match the dashboard use case:

• Queries with a clear time predicate targeting the past six hours (e.g., WHERE time >= ago(6h))
• Aggregate queries (sum, avg, count) paired with dimension filters (e.g., WHERE device_id = 'sensor-001' AND time >= ago(6h))
• Queries that only access data stored in Timestream's in-memory hot tier

4. How to verify only 2% of data is being scanned? #

You can check this in two main ways:

• Query Execution Details: In the Timestream Console, go to the Query History tab, find your query, and view the Bytes Scanned and Bytes Pruned values. Calculate the scan percentage by dividing Bytes Scanned by the total data size (you can get the total table size with the query: SELECT SUM(total_bytes) FROM information_schema.tables WHERE database_name = 'your-db-name' AND table_name = 'your-table-name').
• CloudWatch Metrics: Track the QueryBytesScanned and QueryBytesPruned metrics for your Timestream database. The scan percentage is (QueryBytesScanned / (QueryBytesScanned + QueryBytesPruned)) * 100.

5. Why did your 1-day sum(weight) query scan 50% of the dataset? #

Your result makes sense given the differences between your test and the documentation's benchmark:

• Larger time window: The 2% figure is for 6-hour hot data, but you're querying a full day of data. Most of that 1-day dataset is likely in the cold magnetic storage tier, where pruning is less efficient than in the in-memory tier.
• Missing predicates: If your query only does sum(weight) without additional filters (like device ID, region, or more granular time ranges), Timestream can't prune any data blocks—so it has to scan a much larger portion of the dataset.
• Data partitioning: If your table isn't partitioned or sorted by relevant dimensions (like time + device ID), the query engine can't quickly identify which data blocks contain the weight values you need, leading to broader scans.

备注：内容来源于stack exchange，提问作者Sara Moussa