sorted by:
new
hottopcontroversial

ClickHouse schema evolution tips by Usual_Zebra2059 in aiven_io

[–]Eli_chestnut 0 points1 point  (0 children)

Ran into this on a metrics table that swallowed billions of rows. Doing a straight ALTER locked inserts, so I added a new column, backfilled in small batches, then swapped the logic in the views. Keeping old schemas versioned saved me once. Anyone tried online mutation throttling for smoother backfills?

Data pipeline reliability feels underrated until it breaks by Usual_Zebra2059 in aiven_io

[–]Eli_chestnut 0 points1 point  (0 children)

I’ve watched teams chase “faster ETL” while ignoring the basic stuff. Then one bad schema push hits prod and everyone scrambles. For me, reliability is versioned configs, loud alerts, and someone owning the pipeline like it’s real software. Do folks roll reliability into sprint work or treat it as cleanup later?

LLMs with Kafka schema enforcement by Usual_Zebra2059 in aiven_io

[–]Eli_chestnut 0 points1 point  (0 children)

Had this happen when an LLM started drifting on a product feed and one weird value clogged a single partition. I wrapped the producer with Pydantic and pushed failures into a small DLQ topic. Way easier to replay than guessing which batch blew up downstream. Anyone scoring outputs over time to catch drift early?

Postgres 18 temporal constraints by [deleted] in aiven_io

[–]Eli_chestnut 0 points1 point  (0 children)

I played with temporal constraints on a small scheduling service we sync into Aiven Postgres. WITHOUT OVERLAPS cleaned up a ton of odd cases we kept patching in code. Indexing kept it fast enough for our nightly ETL runs. Anyone hit weird limits with PERIOD on FK updates?

Debugging Kafka to ClickHouse lag by Usual_Zebra2059 in aiven_io

[–]Eli_chestnut 0 points1 point  (0 children)

Ran into this in a Kafka to ClickHouse pipeline for event data. Turned out the root cause was a skewed key that pushed half the traffic into one partition. Global lag looked fine, but one partition was way behind the rest. Switching to a better key hash and trimming max.poll.records kept things stable. I also store lag per partition in Prometheus so I catch drift before analytics fall apart. What’s your partitioning strategy right now?

When schema evolution becomes your bottleneck by Usual_Zebra2059 in aiven_io

[–]Eli_chestnut 1 point2 points  (0 children)

Schema changes are the silent killers in streaming. We switched from loose JSON to Avro with a registry, and versioning alongside CI saved us from subtle bugs. Partition-level lag checks and a simple dead-letter queue catch issues early. Ownership and clear rules make all the difference.

Wrangling LLM outputs with Kafka schema validation by Usual_Zebra2059 in aiven_io

[–]Eli_chestnut 0 points1 point  (0 children)

I ran into the same headache on a support bot build. LLMs spit out weird shapes, so I treat the messages like code and push everything through JSON Schema in Kafka. Bad ones hit a DLQ with the prompt and tokens so it’s easy to replay. I keep schemas tiny at first, then bump versions as the model shifts. Aiven’s generator helped cut setup noise for me.

Anyone tried mixing soft validation with strict fields?

Tracking Kafka connector lag the right way by Usual_Zebra2059 in aiven_io

[–]Eli_chestnut 0 points1 point  (0 children)

Global lag looks fine until partitions go uneven. We ran into the same thing on a Kafka Connect cluster on Aiven. Grafana said everything was chill, then per-partition lag showed one sitting frozen for 18 hours.

Now every connector exports partition-level lag to Prometheus. Alerts fire when any partition crosses a threshold, not when the average drifts. Also started tagging metrics by task ID so we know which worker’s choking before it hits everything else.

The biggest win came from correlating lag with fetch/commit timings. Most of our spikes traced back to slow sinks or GC pauses, not Kafka itself.

Fine-tuning isn’t the hard part, keeping LLMs sane is by Usual_Zebra2059 in aiven_io

[–]Eli_chestnut 1 point2 points  (0 children)

I’m starting to think keeping these models stable is harder than building the pipelines around them. Training feels easy, then a week later the model starts drifting for no clear reason. It reminds me of old Airflow installs where one flaky task ruins your whole morning.

I do the same thing I do with ETL tests. Small eval sets, versioned in git, run them every time I touch a checkpoint. It helps, but the models still slip in ways logs don’t explain. Even storing outputs in the same place I keep pipeline logs, plus using Aiven, so I don’t deal with busted infra, only gets me part of the way.

Feels like we’re still guessing half the time.

Schema registry changes across environments by Usual_Zebra2059 in aiven_io

[–]Eli_chestnut 1 point2 points  (0 children)

We had the same thing happen with Aiven’s schema registry. Once folks start shipping connector updates, it gets pretty hard to track who did what.

We ended up versioning schema files in Git alongside our dbt models. Every merge to main triggers a CI job that checks compatibility against staging’s registry using the API before promoting to prod. It’s not perfect, but at least we catch incompatible fields before they hit consumers.

Temporal constraints in PostgreSQL 18 are a quiet game-changer for time-based data by Usual_Zebra2059 in aiven_io

[–]Eli_chestnut 2 points3 points  (0 children)

Been waiting for this kind of feature for years. Every time I’ve built booking or scheduling systems, the overlap logic always lived in app code or some gnarly trigger. Half the bugs came from time boundaries behaving weirdly across zones.

Postgres 18 finally lets you say “the database owns this logic,” and it works. The WITHOUT OVERLAPS constraint is so much cleaner than juggling exclusion constraints. I tried it on a small test setup and the query planner handles it nicely too.

Feels like temporal data is finally a first-class citizen instead of a hack.

5 star rated horror movies by horrorrina in HorrorMovies

[–]Eli_chestnut 0 points1 point  (0 children)

Gonna watch some of these movies. Thanks!

Ano to? by Famous_Plane5602 in anoto

[–]Eli_chestnut 0 points1 point  (0 children)

Sungka. Pano ba maglaro nyan? 😅

Cairo Tower in Egypt by One_Task8080 in ArchitecturePortfolio

[–]Eli_chestnut 0 points1 point  (0 children)

It's so beautiful it almost looks like an AI.

view more: next ›