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This page contains two benchmarks. The first measures throughput and latency of multiple OTel Collectors writing metric data to OpenData Timeseries. The second measures the throughput and latency of a single Otel Collector writing log data to Clickhouse.

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Writing Metrics to OpenData Timeseries

We ran an 8-hour experiment running three Buffer producers, each producing 10 MB/s (30 MB/s total), with flush and poll intervals of 100 ms. Latency is measured from the producer receiving a data entry to the Buffer consumer reading it.
Latency and throughput holding steady across the 8-hour Buffer benchmark

End-to-end latency and throughput over the 8-hour, 30 MB/s benchmark.

End-to-end latency is tunable: raising the flush and poll intervals lowers cost at the expense of latency, and lowering them reduces latency (to a floor of ~50–100 ms) at increased object-storage request cost.

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Cost

S3 costs over the experiment, projected to a monthly bill:

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OTel → ClickHouse pipeline

This benchmark used the pipelined producer and consumer runtimes to measure the maximum throughput of writing Logs from an Otel Collector to Clickhouse. A single loadgen drove 175,000 log records/sec into one OTel gateway pod over OTLP/gRPC. The gateway ran the OpenData producer, which appended OTel payloads to Buffer on S3. A separate node running the ClickHouse ingestor consumed the payloads and inserted them into ClickHouse. End-to-end latency was measured from two timestamps stamped onto each record (_odb_gateway_received_at at the gateway, _odb_clickhouse_inserted_at at INSERT time). Latency was the difference between these two values, computed via a query on Clickhouse. Both components, the OTel exporter and the ClickHouse ingestor, are documented on the Integrations page.
Throughput holding at 1.1 Gbps with stable end-to-end latency over the run

Throughput and end-to-end latency over the 1.1 Gbps OTel → ClickHouse run.

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Cost

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