QuestDB

Following up on HDFC Bank's announcement from earlier this year, the full case study on real-time transaction monitoring is now out. HDFC Bank, India's largest private bank with more than 90M customers, built an in-house Real-Time Streaming Platform (RTSP) to detect mule accounts: accounts used to receive and move funds obtained through fraud. The platform monitors 25+ banking channels, including UPI, credit cards and internet banking. QuestDB sits at the core of the decisioning path. Some numbers from the write-up: ➖ ~7K transactions per second ingested on a single instance ➖up to 300 rules evaluated per transaction on card channels alone ➖sub-second analytical queries feeding the rules engine and ML models ➖sub-100ms fraud decisions on every transaction ➖6x growth in transaction volumes since deployment The architecture: Kafka and Flink handle filtering, enrichment and aggregation, while QuestDB evaluates rules against historical patterns and serves precomputed aggregates. The underlying hardware is powered by AWS (Kiran Shetty). The part I find most interesting: each channel used to be monitored in isolation. Fraud patterns can now be correlated across all channels on a single platform, and standard SQL means the engineering, risk and fraud teams all work off the same queries. Thanks to Rana Sinha Ray, Zubin Kika, Aru Jindal and the rest of team for the collaboration and for sharing the details publicly. Full case study in the comments #questdb #hdfc #frauddetection #lowlatency #kafka #flink #AWS

Every trading desk wants to know whether its executions are any good. That's what transaction cost analysis is for, and it usually means paying for a separate, proprietary system to do it. You don't need one. If your ticks already sit in #QuestDB, the analysis is just a few SQL queries. We wrote up the whole pipeline, from capturing live ticks through to the summary tables a desk head would read. It captures live BTC-USDT ticks, simulates a TWAP order against them, and measures what that execution cost three ways: Whether the fills beat the market VWAP over the same window. Arrival slippage, fill by fill. An ASOF JOIN matches each execution back to the quote that was live the moment it was scheduled, so you see how far the price had already moved. The markout curve: where the mid went in the seconds and minutes after each fill. This is a HORIZON JOIN, a dozen time offsets per fill in one parallel pass, and it's the part most databases make painful. QuestDB does the SQL, Polars takes the per-fill results into summary tables.

HDFC Bank, the largest private bank in India, is running #QuestDB in production to catch money mule accounts in real time. A mule account is one used to receive and move funds from fraud or other crime, layering the money to hide where it came from. The tell is in the transaction patterns, and those patterns often spread across many channels, which is exactly what makes them hard to see. HDFC uses QuestDB inside its in-house Real-Time Streaming Platform, alongside Kafka and Flink, sustaining 5,000 to 7,000 transactions per second on a single instance. On the card channel alone, up to 300 rules are evaluated per transaction, each one querying historical patterns for that account, all while holding sub-second query latency. What stands out is the unification. Every channel used to be monitored in isolation, so cross-channel fraud patterns were hard to detect and correlate. Routing all channels through QuestDB makes those patterns visible for the first time. Money mule detection is the first use case in production, and volume has grown 6x since launch. Next is Customer Level Monitoring, which moves from near real-time to true real-time: a sub-100ms fraud decision on every transaction before it is approved, rolling out across 25+ channels including UPI, credit cards, and internet banking. The SQL interface let the engineering, risk, and fraud teams collaborate on one platform with familiar tools, which is what accelerated adoption across the bank.

Coming soon to #QuestDB: live views. A live view is a table that stores the incrementally maintained result of a window function over a base table. As rows arrive, the window function runs once per new row and the output is appended, so you get one result row per input row: moving averages, running totals, rankings. You query it like any other table, and it scans precomputed rows instead of recomputing the window on every read. Where materialized views cover the SAMPLE BY side (OHLC bars, downsampled summaries), live views cover the OVER side: the row-per-input window computations. Here is what I ran to show it off. I started an ingestion script pushing 200,000 core prices per second. A couple of refreshes in the web console and we were already past 2 million rows. Then one statement, a CREATE LIVE VIEW with a moving average of the price. To show the read side, I opened three blotters against the view: one on localhost, one on a separate instance, one in the browser. All three updated in real time, around 10 times per second. The part I like most: a live view keeps its freshest computed rows in an in-memory tier and only persists them to its own disk tier on the FLUSH EVERY cadence. A full-row SELECT reads that in-memory tier, so the view reflects the latest computed prices before they are flushed to disk. FLUSH EVERY controls durability, not freshness. Standard SQL, streaming analytics, without leaving the database. More to come as we get closer to release.

Brevan Howard, a global alternative asset manager, uses QuestDB for its live and historic market data offering within its systematic trading business. Why QuestDB? A low-latency database built to store market data and trades at high speed, with an out-of-the-box pre-trade analysis toolkit to react to markets fast, and the resiliency needed for mission-critical data. During a typical US trading session, up to 200,000 messages a second come in from the street, ingested and queryable in real time, then tiered down to Parquet format for lakehouse access and efficient long-term retention. It's been a pleasure working with Michael Repasky, Stuart Masterton-Smith and Yelzhan Zhangarin. #brevanhoward #questdb #tickdata #systematictrading #marketdata

Financial returns are rarely normal. Fat tails and asymmetry are the norm, not the exception, and until now measuring them in #QuestDB meant exporting to pandas. We just added skewness and kurtosis as native aggregate functions. Both follow Fisher's definition, so a normal distribution has skewness 0 and excess kurtosis 0. Positive kurtosis means heavier tails than normal, positive skewness means a longer right tail. Also in the docs: • skewness / skewness_samp: sample skewness using the bias-corrected Fisher-Pearson G1 estimator, null below three non-null values • skewness_pop: population skewness, no bias correction • kurtosis / kurtosis_samp: sample excess kurtosis with Fisher-Pearson bias correction, null below four non-null values • kurtosis_pop: population excess kurtosis, no bias correction They work like any other aggregate, so you can compute the third and fourth moments per symbol, per venue, or per time bucket without leaving the database.

Search for "the best time-series database" and you get the same article over and over: a few engines lined up and ranked, almost entirely on speed. It's an odd way to choose one. Fast ingestion and fast queries are the reason the category exists. So we wrote the other guide. The parts of the decision you actually live with once the data is flowing: Does the query language really understand time? An ASOF JOIN matches each row to the most recent prior row in another series. ClickHouse, which isn't a time-series database, has one. InfluxDB 3 and TimescaleDB, which are, don't offer a native one. How much do you have to operate day to day? Everything runs fine on one node. Surviving a node failure is where "open source" and "easy" part ways. Where does the free version actually stop? ClickHouse and TDengine put clustering and HA in open source. InfluxDB 3 Core is single node with a roughly 72-hour default query window. DolphinDB Community caps you at two nodes and two cores. #QuestDB is Apache 2.0 at the core, and replication and HA are Enterprise. Every engine here draws the line somewhere. Know where before you commit. How openly is your data stored? Data already in an open format, in your own account, never needs migrating. Seven engines: QuestDB, InfluxDB, TimescaleDB, ClickHouse, kdb+, TDengine and DolphinDB. One blog post. Disclaimer: we build QuestDB, so we're biased and we say so in the post. We checked every claim against each vendor's own documentation, and if we got something wrong, tell us and we'll fix it. It's a guide, not a leaderboard.

When you write (x << 2) & -4, the bitwise AND does nothing. The low two bits are already zero after the shift, so the mask clears nothing. For most of HotSpot's life the JIT could not see that. Now it can. Jaromir Hamala wrote up how C2, the JVM's optimizing compiler, learned to reason about individual bits through a known-bits abstraction: the same tristate-bit idea that LLVM and GCC already use. It tracks which bits are definitely 0, definitely 1, or unknown, lets a value range and a bitmask refine each other, and uses the result to delete redundant masks and shifts. The post walks through the actual C2 source, the transfer functions for AND and shift, and the disassembly showing the AND instruction disappear between JDK 24 and JDK 27. There is also a nice section on how they test it: shrink the integer width down to 4 bits so you can brute-force every possible state. This is the kind of low-level detail the #QuestDB team enjoys chasing, and we are built on the JVM, so this is exactly where it shows up.

High availability usually comes with operational complexity. We’re changing that in #QuestDB Enterprise. Promoting a replica to become the new primary will soon be as simple as executing: SWITCH ROLE TO PRIMARY; or calling a /switch REST endpoint. The best part is what doesn’t happen: the instance doesn’t restart. The node changes role while continuing to run, making failover a straightforward, automatable operational task instead of a disruptive maintenance procedure. Coming soon.

Building dashboards with an AI agent usually means copying queries back and forth between tools, or using temporary python files the agent needs to write to call some API and then discard. The new #QuestDB MCP Bridge removes that step by connecting your coding agent directly to the QuestDB Web Console. Your agent can create notebook cells, execute SQL, and build charts in your existing browser session. The dashboard below was assembled this way from live FX data, ready for you to inspect, modify, and extend. Instead of asking an agent to generate SQL and then pasting it into a dashboard, you can have it build the dashboard itself. Link in first comment 👇