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Weaviate

Weaviate

Observe your semantic cache efficiency to reduce cost and latency for LLM apps

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Overview

Vector databases, such as Weaviate play an important role as semantic caches within state-of-the-art Large Language Model (LLM) service stacks.

Semantic caches reduce the latency for well-known and frequent user prompts while at the same time help to reduce the overall cost of pre-trained model cloud service costs. Monitoring cache efficiency and memory utilization ensures optimal resource allocation, while adaptability to dynamic contexts measures the cache's ability to respond accurately to evolving conversation dynamics. Additionally, cache warm-up times contribute to faster availability of cached information. For vector databases, query performance and indexing speed are critical indicators, directly impacting the system's ability to handle similarity searches efficiently. Scalability, accuracy of vector representations, and storage efficiency are essential for managing growing datasets effectively. Factors such as update, and deletion performance and query throughput further contribute to the overall effectiveness of these systems in providing real-time and accurate responses in natural language processing and similarity search applications. Striking the right balance across these KPIs ensures that both semantic LLM caches and vector databases deliver optimal performance across diverse use cases. In summary, vector databases such as Weaviate aim to address performance-related challenges, enhance efficiency, and contribute to a more seamless and responsive experience in various natural language processing applications.

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Setting up full stack observability for your GenAI applications is possible with Traceloop's OpenLLMetry, with OpenTelemetry under the hood, which can seamlessly provide comprehensive end-to-end insights into your production environments.

To set up OpenLLMetry with Dynatrace, see Dynatrace Documentation.

Details

Weaviate can expose Prometheus-compatible metrics for monitoring. A standard Prometheus setup can be used to visualize metrics on various dashboards in your Dynatrace environment.

Weaviate metrics are then used to measure request latencies, import speed, time spent on vector vs object storage, memory usage, application usage, and more.

Beside other measurements, Weaviate exposes following metrics that allow users to observe the health and performance of their vectorized index:

  • batch_durations_ms: a histogram type metric that represents the duration of a single batch operation in ms. The dimension ‘operation’ defines which operation the batch was performing (e.g.: object, inverted, vector) and the ‘class_name’ dimension specifies the class the batch was affecting.
  • batch_delete_durations_ms: a histogram type metric that represents the duration of batch deletions, where the dimension ‘operation’ further refines what operation was measured.
  • objects_durations_ms: a histogram type metric that represents the duration of individual operations.
  • object_count: a gauge type metric that represents the number of objects per object class.
  • async_operations_running: a gauge type metric that represents the number of currently running async operations. The operation itself is defined through the operation dimension.
  • lsm_active_segments: a gauge type metric that represents the number of currently present segments per shard within the Log-Structured-Merge Tree (LSM).
  • lsm_bloom_filter_duration_ms: a histogram type metric that represents the duration of a bloom filter operation per shard in ms within the Log-Structured-Merge Tree (LSM).
  • lsm_segment_objects: a gauge type metric that represents the number of entries per Log-Structured-Merge Tree (LSM) segment by level.
  • lsm_segment_size: a gauge type metric that represents the Log-Structured-Merge Tree (LSM) segment size.
  • lsm_segment_count: a gauge type metric that represents the number of Log-Structured-Merge Tree (LSM) segments.
  • vector_index_tombstones: a gauge type metric that represents the number of currently active tombstones in the vector index. Will go up on each incoming delete and go down after a completed repair operation.
  • vector_index_tombstone_cleanup_threads: a gauge type metric that represents the number of currently active threads for repairing/cleaning up the vector index after deletes have occurred.
  • vector_index_tombstone_cleaned: a counter type metric that represents the total number of deleted and removed vectors after repair operations.
  • vector_index_operations: a gauge type metric that represents the total number of mutating operations on the vector index. The operation itself is defined by the operation label.
  • vector_index_size: a gauge type metric that represents the total capacity of the vector index.
  • vector_index_maintenance_durations_ms: a histogram type metric that represents the duration of a sync or async vector index maintenance operation. The operation itself is defined through the operation label.
  • vector_index_durations_ms: a histogram type metric that represents the duration of regular vector index operation, such as insert or delete. The operation itself is defined through the operation label. The step label adds more granularity to each operation.
  • startup_durations_ms: a histogram type metric that represents the duration of individual startup operations in ms.
  • startup_diskio_throughput: a histogram type metric that represents the Disk I/O throughput in bytes/s at startup operations, such as reading back the HNSW index or recovering LSM segments.
  • requests_total: a gauge vector type metric that tracks all user requests to determine if it was successful or failed.
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