When IoTDB was initiated in 2011, almost all influential distributed systems and databases were built in Java or on the JVM—such as Hadoop, HBase, Spark (Scala on JVM), Cassandra, Kafka, and Flink. To integrate deeply with the big data ecosystem, choosing Java was a natural decision. - Source: dev.to / 4 months ago

In the meantime, other query engine support is on the roadmap, including Apache Spark, Apache Flink, and others. - Source: dev.to / 12 months ago

Many stream processing systems today still rely on local disks and RocksDB to manage state. This model has been around for a while and works fine in simple, single-tenant setups. Apache Flink, for example, uses RocksDB as its default state backend - state is kept on local disks, and periodic checkpoints are written to external storage for recovery. - Source: dev.to / about 1 year ago

Because the hosted catalog is a standard JDBC catalog, tools like Spark, Trino, and Flink can still access your tables. For example:. - Source: dev.to / about 1 year ago

I wrote a python based aircraft monitor which polls the adsb.fi feed for aircraft transponder messages, and publishes each location update as a new event into an Apache Kafka topic. I used Apache Flink — and more specially Flink SQL, to transform and analyse my flight data. The TL;DR summary is I can write SQL for my real-time data processing queries — and get the scalability, fault tolerance, and low latency... - Source: dev.to / about 1 year ago

Durable execution — the pattern implemented by Temporal, Inngest, Rivet Actors, and now Cloudflare Workflows — treats waiting as a continuation rather than a loop:. - Source: dev.to / 5 days ago

Two specific moves stand out in Duncan's account. The first is durable execution, via Temporal — Mercury replaced fragile cron-and-database state machines with workflow code whose failure semantics are platform-handled (replay, retry, timeout, cancellation). Mercury open-sourced its hs-temporal-sdk, which wraps Temporal's official Rust Core SDK via FFI and provides a Haskell-native API. The dovetail with Haskell's... - Source: dev.to / about 1 month ago

We picked Temporal as the first reference engine on purpose. Temporal has the strictest execution model we know of – a V8 sandbox, determinism constraints, replay-driven recovery. If our port contract holds up against that, easier engines – an in-memory test double, a BullMQ queue, or JSON-first platforms like Inngest or Restate – plug in through the same two interfaces. We're shipping Temporal first; the rest is... - Source: dev.to / about 2 months ago

The trick is to find whatever metadata channel the queue already gives you and use that and thankfully, almost every mature queue has one (probably because of this scenario). SQS has message attributes, Temporal has context propagators built into the SDK, and Hatchet (which we use to run our workflows) has a metadata field called additionalMetadata. - Source: dev.to / 3 months ago

A typical production stack for teams using Claude or Gemini as the reasoning layer includes an LLM provider API, an orchestration layer (n8n, Temporal, or a custom Python service), application infrastructure (a server running the orchestration code), and a data layer (a database for storing results). Each boundary introduces a failure point. When the LLM provider changes its rate limits, as OpenAI did repeatedly... - Source: dev.to / 4 months ago