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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
Datadog bought Timber Technologies (creators of Vector) two years ago. https://www.datadoghq.com/blog/datadog-acquires-timber-techn...
Timber definitely intended to just rock out & demolish everything else out there with their agent/forwarder/aggregator tech. But it wasn't a competitive play against OTel, in my humble opinion. Timber's whole shtick is that it integrates with everything, with really flexible/good glue logic in-between. A competent multi-system (logging, metrics, eventually traces) fluentd++. OTel - I want to believe - would have been part of that original vision.
It's just taking a really really long time. One can speculate how direction & velocity might have changed since the Datadog acquisition. The lack of tracing (anywhere except Datadog, so far) materializing has been a hard hard hard & sad thing to see. OG https://github.com/vectordotdev/vector/issues/1444 and newer https://github.com/vectordotdev/vector/issues/17307
We're building something similar at Tenzir, but more for operational security workloads. https://docs.tenzir.com
Differences to Vector:
- An agent has optional indexed storage, so you can store your data there and pick it up later. The storage is based on Apache Feather, Parquet's little brother.
- Pipelines operators both work with data frames (Arrow record batches) or chunks of bytes.
- Structured pipelines are multi-schema, i.e., a single pipeline can process streams of record batches with different schemas.
Related posts
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What is a low/reasonable cost solution for service log storage and querying?
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Docker Log Observability: Analyzing Container Logs in HashiCorp Nomad with Vector, Loki, and Grafana
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Hacks to reduce cloud spend
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creating a centralize syslog server with elastic search
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Retaining Logs generated by service running in pod.