Bump org.questdb:questdb from 9.2.2 to 9.3.3 in /modules/questdb

[dependabot]

Bumps org.questdb:questdb from 9.2.2 to 9.3.3.

Release notes

Sourced from org.questdb:questdb's releases.

9.3.3

QuestDB 9.3.3

QuestDB 9.3.3 is a feature-rich release introducing HORIZON JOIN for markout analysis, a new twap() aggregate, SQL-standard WINDOW definitions, JIT compilation on ARM64, and file-based secrets for Kubernetes deployments. It also brings significant performance improvements across Parquet I/O, parallel GROUP BY, UNION queries, and ORDER BY on computed expressions.

For any questions or feedback, please join us on Slack or on Discourse.

See also our prettier release notes page.

Highlights

HORIZON JOIN for markout analysis

HORIZON JOIN is a new join type designed for markout analysis — a common financial analytics pattern where you measure how prices or metrics evolve at specific time offsets relative to events like trades or orders.

For each row in the left-hand table and each offset in the horizon, the join computes left_timestamp + offset and performs an ASOF match against the right-hand table. Results are implicitly grouped by the horizon offset and any specified keys, with aggregate functions applied across all matched rows.

Here's an example measuring post-trade price impact at 1-second intervals up to 60 seconds:

SELECT h.offset / 1_000_000 AS horizon_sec, t.sym, avg(m.mid) AS avg_mid
FROM trades AS t
HORIZON JOIN mid_prices AS m ON (t.sym = m.sym)
RANGE FROM 1s TO 60s STEP 1s AS h
ORDER BY t.sym, horizon_sec

You can also use LIST for non-uniform horizons and negative offsets to look at pre-event behavior:

SELECT h.offset / 1_000_000 AS horizon_sec, t.sym,
       avg(m.mid - t.price) AS avg_markout
FROM trades AS t
HORIZON JOIN mid_prices AS m ON (t.sym = m.sym)
LIST (-5s, -1s, 0, 1s, 5s, 30s, 1m) AS h
ORDER BY t.sym, horizon_sec

The horizon pseudo-table exposes h.offset (raw microsecond value) and h.timestamp (the computed left_timestamp + offset), which can be used in expressions and grouping.

twap() time-weighted average price

The new twap(price, timestamp) aggregate computes the time-weighted average price using step-function integration: each price is held constant until the next observation, and the TWAP is the area under the step function divided by the total time span. It supports parallel GROUP BY and SAMPLE BY with FILL modes.

SELECT symbol, twap(price, timestamp) AS twap_price
FROM trades
WHERE timestamp IN today()
SAMPLE BY 1h;

... (truncated)

Commits

• 0a63ba5 [maven-release-plugin] prepare release 9.3.3
• c371fdc bump web console to 1.2.0
• 8ab362f chore(sql): speed up horizon join time frame search for large RHS tables (#6814)
• 6c74d1f chore(parquet): update stale Rust libraries with decimal support for parquet ...
• a77a48c fix(sql): fix AssertionError on multi-column JOIN with table-qualified key na...
• 0c26da4 chore(core): add missing authorization checks for alter table and materialize...
• 8205f87 perf(sql): avoid temp table materialization for Parquet export with computed ...
• 48f8b0b test(core): stabilize flaky testCheckpointListenerOnReleased() (#6804)
• f321e3b fix(sql): NullPointerException when right-hand query in window join has times...
• f353964 fix(http): fix server crash on chunked transfer encoding with overflowing chu...
• Additional commits viewable in compare view

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