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Greptime

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@Greptime

The open-source observability database. One columnar engine for metrics, logs & traces on object storage. SQL across signals. PromQL for metrics.
Wolfe Road Sunnyvale greptime.com Joined May 2022
198 Following 1K Followers 950 Posts

Latest posts 5

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Greptime Verified account @Greptime
GreptimeDB v1.1: you no longer have to lock in a table's partition layout when you create it.

Before, only tables created with PARTITION ON COLUMNS could be resharded. Now you can take an existing single-Region table and split it online:

ALTER TABLE sensor_readings PARTITION ON COLUMNS (device_id, area) (...)

Repartition as data grows, instead of guessing up front.

Full blog in detail: greptime.com/blogs/2026-06-…
GreptimeDB v1.1.0 is out, a production-focused release after the v1.0 GA
Greptime Verified account @Greptime
Metrics, logs, and traces in one explore view, with one time range and one set of label filters across all three. Signals Explore ships in the GreptimeDB v1.3 dashboard.

Click a trace_id in a log line and the whole trace opens next to it.

The live demo runs on the OpenTelemetry official shop Demo:
tinyurl.com/4j7f2k8h
Card headline: "Metrics, logs, traces. One explore view." Three tabs share one time range and one set of label filters, with no PromQL or SQL to write. Three stacked screenshots from the live demo: Metrics, 585 metrics grouped by prefix with rate charts; Logs, product-catalog service logs with trace_id links and a volume chart; Traces, rate, errors, and a duration heatmap. Footer: live demo data from the OpenTelemetry Demo.
Card headline: "Click a trace_id in a log. Get the whole trace." Two real screenshots from the GreptimeDB v1.3 dashboard live demo. Behind: Signals Explore, Logs tab, logs from opentelemetry_logs grouped by resource_attributes.service.name, with timestamped log lines. In front: a trace timeline for trace b7c300d24c59b6df91498a2a9858c99e with 9 spans across frontend-web, frontend-proxy, frontend, and cart, from a 59 ms GET down to a 0.22 ms valkey-cart call. A label reads "trace_id → timeline".
Greptime Verified account @Greptime
High-cardinality telemetry and fast access to historical data are both essential for observability at scale.

Our co-founder shares how OpenAI uses GreptimeDB to address these challenges for Spark—and make the data more accessible to AI agents. 👇
Dennis Zhuang @killme20082 4d
#OpenAI gave two talks at Community Over Code EU 2026 on running #Spark at their scale: hundreds of thousands of applications a day. Both end up at GreptimeDB for Spark observability, from opposite ends.

One wanted metrics for every Spark task. That's up to tens of millions of new series a day, with logs, metrics and events sitting in separate stores.

The other needed the Spark History Server fast enough for agents. Opening a 100 GB+ event log could take up to half an hour, and every agent tool call waited on it.

Where it landed: a listener in the Spark driver writes jobs, stages, tasks and executors into #GreptimeDB. The History Server reads from it. The #Flink job that parsed event logs, "probably one of the most expensive Flink apps at OpenAI", is no longer needed. The data sits on S3 as #Parquet, so Spark and Trino query it directly.

My read: high cardinality and scattered signals are one problem. They show up together once telemetry is fine-grained enough to be useful.

Our notes on both talks, with timestamps: greptime.com/blogs/2026-11-…
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