DOP-C02 · Question #210
A company is developing an application that will generate log events. The log events consist of five distinct metrics every one tenth of a second and produce a large amount of data. The company…
The correct answer is A. Use batch writes to write multiple log events in a single write operation. D. Treat each log as a multi-measure record. E. Configure the memory store retention period to be longer than the magnetic store retention. To achieve the fastest query performance for high-volume log events in Amazon Timestream, use batch writes, treat multiple metrics per log as a multi-measure record, and configure the memory store retention to keep frequently queried data in the faster memory tier.
Question
A company is developing an application that will generate log events. The log events consist of five distinct metrics every one tenth of a second and produce a large amount of data. The company needs to configure the application to write the logs to Amazon Timestream. The company will configure a daily query against the Timestream table. Which combination of steps will meet these requirements with the FASTEST query performance? (Choose three.)
Options
- AUse batch writes to write multiple log events in a single write operation.
- BWrite each log event as a single write operation.
- CTreat each log as a single-measure record.
- DTreat each log as a multi-measure record.
- EConfigure the memory store retention period to be longer than the magnetic store retention
- FConfigure the memory store retention period to be shorter than the magnetic store retention
How the community answered
(37 responses)- A62% (23)
- B11% (4)
- C5% (2)
- F22% (8)
Why each option
To achieve the fastest query performance for high-volume log events in Amazon Timestream, use batch writes, treat multiple metrics per log as a multi-measure record, and configure the memory store retention to keep frequently queried data in the faster memory tier.
Batch writes reduce the overhead of individual write operations, making the ingestion of a large volume of log events more efficient and faster, aligning with the large data production.
Writing each log event as a single write operation creates significant overhead and is inefficient for high-volume data ingestion, negatively impacting write performance and cost.
Treating each log as a single-measure record would require creating five separate records for each log event, increasing data volume and complicating queries that need to correlate these metrics.
Treating the five distinct metrics as a multi-measure record significantly reduces the number of records written and read for a given timestamp, grouping related data efficiently for improved query performance.
Configuring a sufficiently long memory store retention period ensures that the data relevant for daily queries remains in the faster in-memory tier, which is crucial for achieving the fastest query performance on recent data.
Configuring a shorter memory store retention period would cause data to move to the slower magnetic store sooner, degrading query performance for data that might still be relevant for daily analysis.
Concept tested: Amazon Timestream data modeling, write strategies, and storage optimization
Source: https://docs.aws.amazon.com/timestream/latest/developerguide/data-modeling.html
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