Questions that come up constantly around Spark, dbt, and Flink pipelines — the same ones that shaped what opti-pipe's rule engine actually checks for.
spark-submit's --deploy-mode flag decides where the driver runs - and client mode quietly ties a production job's survival to whatever machine happened to submit it.
Spark's execution engine is identical no matter what schedules its executors. The real question is who owns provisioning and failure recovery - Spark, you, or a vendor.
Session mode and application mode trade off in exactly one dimension: whether a misbehaving job can take other jobs down with it. Here's when each is the right default.
Flink's native Kubernetes integration means Flink itself talks to the Kubernetes API to manage its own TaskManager pods - a meaningfully different deployment than just running Flink in a container.
dbt run just runs once and exits - dbt has no opinion on scheduling. Here's what dbt Cloud, a self-hosted orchestrator, and plain cron each actually handle for you.
dbt splits "what to build" from "where to connect" into two files on purpose. Here's how profiles.yml and env_var() are supposed to keep credentials out of git.
A model can run cleanly against a table that stopped updating three days ago. Here's how sources.yml and dbt source freshness catch that instead of staying quiet.
Flink's defaults get a job running fast in dev, not surviving a real restart with real state size. Here's how the state backend and checkpoint storage settings actually work.
Flink's default restart behavior is built for an occasional hiccup, not a genuinely broken deploy. Here's how fixed-delay, exponential-delay, and failure-rate actually differ.
spark.dynamicAllocation.enabled=true alone usually does nothing. Here's the shuffle service prerequisite and the min/max bounds that actually make dynamic allocation work.
The Spark UI is just a renderer for this file. Here's what's actually inside it, and the jq one-liner that gets you the same numbers faster.
Every dbt run writes this file to target/, whether or not you ever open it. It already contains the answer to “which model is slow.”
“It finished without errors” and “we used the cluster we're paying for” are two different claims. Here's how to tell which one you're actually looking at.
A broadcast join is a bet that one side of the join is small enough to copy everywhere. When the bet is right it's the fastest join Spark has - when it's wrong, it's the fastest way to OOM a driver.
A job can be I/O-bound and look compute-bound on every dashboard that only tracks CPU - because the real bottleneck is opening files, not reading bytes.
Flink's metrics API returns dozens of counters per task. Here's the short list that actually answers “is this job okay,” and why the rest can wait.
A checkpoint that quietly times out under load doesn't crash the job - it just leaves you with a much larger recovery window than you think you have.
A schema change or a misconfigured on_schema_change can silently force a full table rebuild on every run - no error, just a much longer run and a bigger bill.
Speculative execution re-runs slow tasks on a hunch they're stragglers. That's a good bet for a flaky node and a bad one for data skew - here's how to tell which you have.
A driver OOM and an executor OOM have almost nothing in common as causes, even though the fix everyone tries first is the same: bump the memory setting.
ClickHouse just became the first partner-built v2 adapter on the dbt platform, powered by dbt's new Rust-based Fusion engine — here's what's actually usable today.
Nine times out of ten it isn't the code. It's the data, the cluster, or a config that quietly stopped matching either one.
There's no universal right answer, but there is a defensible starting point — and, more usefully, a way to tell when your current numbers are wrong.
"Just increase executor memory" fixes the symptom about half the time and wastes money the other half. Here's how to tell which case you're in.
Not every cost lever carries the same risk. Here's the order that gets you real savings before you touch anything that could actually break a pipeline.
Skew is invisible in aggregate metrics and obvious in per-task metrics — you just have to be looking at the right view.
The right cluster size isn't a number you pick once — it's a policy you set from how your workload actually behaves, then revisit as that behavior changes.
In a dbt project, run time and warehouse bill are almost the same metric wearing two names — fixing one usually fixes the other.
Backpressure isn't a bug to eliminate — it's Flink correctly telling you where your pipeline's real bottleneck is.
Dev doesn't lie to you exactly — it just answers a different question than the one that matters in production: "does this work," not "does this work at 50x the data."
Full observability stacks solve this, but they're a lot of platform for a question that usually comes down to four or five numbers trending the wrong way.
The bill doesn't spike from one bad job — it creeps from a handful of settings tuned once during rollout and never revisited. Here's where to look first.
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