QuestDB vs kdb+

QuestDB Overview

QuestDB is an open-source time-series database purpose-built for capital markets, delivering low-latency ingestion and queries on market data. Optimized for performance-critical environments, its multi-tiered storage engine outputs Parquet files for downstream workflows and integrates easily with AI/ML tools. With native SQL support, QuestDB combines speed and simplicity for modern trading infrastructure.

kdb+ Overview

kdb+ is a proprietary columnar database developed by KX Systems, particularly popular in high-frequency trading and financial analytics. It stores data in a proprietary binary format and uses the q programming language, a domain-specific functional language designed for time-series analysis. kdb+ is known for its in-memory processing capabilities and vectorized operations.

Ecosystem & Openness

Data Format

Open

Parquet/Arrow native, PGWire

Proprietary

On-disk binary format

Data Portability

Files are readable by any Arrow/Parquet-capable engine, no vendor lock-in

Data stored in proprietary format, export needed for interoperability

Total Cost of Ownership

Low

Historical data stored as compressed Parquet on object storage

High

Expensive local storage footprints & data duplication requirements

Capital Markets Integrations

Standard protocols

REST, Kafka, SQL, ILP - broad market data vendor feed support in development

Native connectors

Pre-built feed handlers, native FIX protocol support

Data Processing & Architecture

Data Science Processing

Arrow offload or native access to Parquet with Python tooling (Pandas, PyArrow, Polars)

Built-in q vector language, Python (PyKX), Java, R, REST

Storage Strategy

Memory + Disk

Disk-backed column files memory-mapped for near-RAM speed

Memory + Disk

Most-recent data in an in-memory RDB; historical data memory-mapped from disk

Memory Requirements

Low baseline

Memory scales with working set only

High memory

Real-time installs often keep large data sets in RAM

Distributed Architecture

Scales to N-replicas or shards behind object storage

Core engine is single-process, optional KDB Insights overlay adds sharding and autoscaling

Concurrency & Data Safety

Multiple Concurrent Access

Multi-threaded reads and writes: readers never block, single writer thread per table prevents conflicts

Multi-thread query mode exists, only one process may write a table, extra writers risk corruption

Data Safety & Integrity

Keeps a stable on-disk snapshot: after a crash, only the last few seconds of WAL need replaying

Intra-day data is stored in memory: after a crash, the server needs to replay all the logs since the beginning of the day

Security & Language

Security

TLS on by default; ACL & RBAC, open-source CVEs

No auth/TLS out-of-the-box; manual hardening or Insights add-on; no public CVEs

Query Language

SQL

Standard SQL with capital-markets extensions

q

Vector DSL; highly expressive but requires specialist training and a steep learning curve

Key Differences

Open vs Closed Data Formats and Ecosystem

QuestDB uses a multi-tiered storage engine—Write-Ahead Log for durability, a high-performance proprietary column store for hot data, and compressed Parquet files for cold data on object storage. It can also egress data natively in Apache Arrow via ADBC, ensuring data freedom and accessibility by any engine or tool without vendor lock-in. Historical data stays cost-effective yet fully queryable in place.

kdb+ does store tables in its own proprietary binary layout that is memory-mapped directly by the q engine. Accessing those files without running kdb+ is non-trivial, which creates a form of vendor dependency. kdb+ also provides pre-built connectors for market data vendors.

Data Portability and Total Cost of Ownership

QuestDB's open format approach means files are readable by any Arrow/Parquet-capable engine, eliminating vendor lock-in concerns. The open Parquet format significantly reduces total cost of ownership by eliminating the need to duplicate data for reuse across different workflows.

kdb+ stores data in a proprietary binary format that requires data duplication when the same data needs to be accessed by different systems, leading to higher operational costs and storage complexity.

Multiple Concurrent Access and Data Safety

QuestDB supports fully-multi-threaded reads and writes—readers never block, and a single writer thread per table prevents conflicts. A write-ahead log plus lightweight, always-on checkpoints keep a stable on-disk snapshot, so after a crash only the last few seconds of WAL must be replayed.

kdb+ allows many parallel readers, but safe operation still follows a one-writer-per-table rule; extra writers can corrupt files, so scaling concurrent write workloads requires external orchestration.

Memory Requirements and Storage Strategy

QuestDB uses a disk-backed approach with memory-mapped column files for near-RAM speed performance. Memory scales with the working set only, resulting in lower baseline memory requirements and reduced operational complexity.

kdb+ keeps most-recent data in an in-memory RDB with historical data memory-mapped from disk. Real-time installations often require keeping large datasets in RAM, leading to high memory requirements that can overwhelm system resources.

Distributed Architecture and Scalability

QuestDB provides native distributed capabilities, scaling to N-replicas or shards behind object storage without bottlenecks on the primary instance.

kdb+ core engine is single-process, with optional KDB Insights overlay adding sharding and autoscaling. The base system lacks built-in distributed capabilities, requiring manual clustering setup.

Security and Enterprise Readiness

QuestDB delivers enterprise-grade security with TLS enabled by default, fine-grained ACL & RBAC controls, Single Sign-On with EntraID/LDAP/OAuth, and transparent CVE reporting. As open-source software used by hundreds of thousands of developers, its codebase is publicly visible and continuously scrutinised by the community.

kdb+ provides no authentication or TLS out of the box, requiring manual hardening or the optional Insights add-on for security features. The absence of publicly disclosed CVEs leaves security teams with less external visibility when assessing potential vulnerabilities.

Query Language Accessibility

QuestDB uses standard SQL enriched with specialised time-series extensions (e.g., SAMPLE BY, LATEST ON) and a library of capital-markets functions (e.g., VWAP, l2price). This keeps the interface familiar for most developers and analysts while covering domain-specific analytics, minimising training time and accelerating productivity.

kdb+ requires the q programming language, a terse vector-based DSL that demands specialist training and can create knowledge silos within organizations. At the same time, q is a dynamic, functional programming environment that extends beyond simple querying, offering advanced flexibility for users deeply familiar with its paradigm.

Example Query Comparison: OHLCV from trades table

SELECT
    symbol,
    first(price) AS open,
    max(price) AS high,
    min(price) AS low,
    last(price) AS close,
    sum(amount) AS volume
FROM trades
WHERE timestamp IN today()
SAMPLE BY 5m;

select
  open: first price,
  high: max price,
  low: min price,
  close: last price,
  volume: sum amount
by sym, 5 xbar time.minute
from trades
where date timestamp = .z.D

t: update bucket: 5 xbar time.minute from trades
c: (=; (date; `timestamp); .z.D)
b: `sym`bucket
a: `open`high`low`close`volume!(
  (first; `price);
  (max;   `price);
  (min;   `price);
  (last;  `price);
  (sum;   `amount)
)
result: ?[t;c;b;a]

Conclusion

For modern capital markets workloads that demand millisecond latency, QuestDB's low-latency architecture delivers superior ingestion speed and sub-millisecond queries via standard SQL, open Arrow/Parquet storage, and enterprise-grade security—whether deployed on-premises or in the cloud.

Open data formats slashes total cost of ownership, reduces operational complexity, and lets institutions tap the vast talent pool of SQL-and Python-native engineers who can pipe QuestDB data straight into Pandas, Polars, DuckDB, or any Arrow-aware AI workflow.

While kdb+ can still serve legacy estates already invested in q, its proprietary stack and niche skill requirements limit integration and agility. QuestDB therefore represents the future of financial time-series databases—delivering institutional-grade performance without vendor lock-in, scarce-skill dependencies, or prohibitive costs.