Kafka is excellent at storing and moving events. But storing events is not the same as understanding them.

Kafka holds the events

Apache Kafka stores streams of events, such as orders, payments, clicks, or sensor readings, in topics. Producers write events into topics, and consumers read them out. This gives you a durable, ordered, replayable record of what happened in your business.

But what if you want answers?

Very often, teams want more than raw events. They want to count orders per minute, filter out test traffic, enrich each order with customer details, detect unusual patterns, or keep an always-fresh summary of the latest state. To do this with plain Kafka, a developer must write a program that reads events, processes them, keeps track of running totals, handles failures, and writes results back. This takes time, skill, and careful testing, even for a simple question.

A gap between people and streams

Many people who understand the business best, such as analysts and data engineers, know SQL but do not write stream-processing programs in a programming language. For them, live event streams feel like a locked room. ksqlDB was created to open the door: it lets you describe what you want from your streams in SQL-like statements, and the system does the heavy lifting.

ksqlDB is a database purpose-built for stream processing applications on top of Apache Kafka.

The simple definition

ksqlDB is a server that lets you create continuously running queries over Kafka topics, using a SQL-like language. You describe the streams you have, the transformations you want, and the results you need. ksqlDB runs those queries forever, reading new events as they arrive, processing them, and writing results to new Kafka topics. It can also keep summary tables up to date and answer questions about their current contents on demand.

Where it comes from

ksqlDB was created by Confluent, the company founded by the original creators of Kafka. It was first called KSQL, and later evolved into ksqlDB with added abilities such as answering lookups directly. Internally, it is built on Kafka Streams, the stream-processing library that ships with Kafka, so it inherits that library’s approach to scaling and fault tolerance.

Three big ideas

flowchart LR
  T1["Kafka topic: orders"] --> KS["ksqlDB server"]
  T2["Kafka topic: customers"] --> KS
  KS --> T3["Kafka topic: enriched-orders"]
  KS --> T4["Kafka topic: orders-per-minute"]
  U["Analyst using SQL-like statements"] --> KS
        

Everything in ksqlDB is built from two simple objects. Understanding them is the key to the whole tool.

What is a stream?

A stream is an endless, ordered series of events where every event is a new fact. Each order placed, each click made, and each temperature reading is one event in the stream. Events are only added, never changed. If a customer places three orders, the stream holds three separate events. A stream answers the question “what happened?”

What is a table?

A table holds the latest value for each key. Think of a table of customer addresses, where each customer has exactly one current address. When a customer moves, the row for that customer is updated. A table answers the question “what is the current state?” Behind the scenes, a table is also built from a stream of changes, where each new event with a key replaces the previous value for that key.

The stream-table duality

These two views are two sides of the same coin. A table can be turned into a stream by recording every change made to it. A stream can be turned into a table by keeping only the latest event for each key, or by summarising it, such as counting. ksqlDB lets you move between them freely, which is one of its most powerful ideas.

flowchart LR
  S["Stream: every purchase event"] -->|"Count per customer"| T["Table: purchases so far per customer"]
  T -->|"Every change becomes an event"| S2["Stream of changes"]
        

ksqlDB offers three main kinds of queries, and each answers a different kind of need.

One of the most useful things ksqlDB can do is keep a summary table permanently up to date.

What is a materialised view?

A materialised view is a table whose contents are calculated from a stream and updated automatically with every new event. For example, a table that holds the total number of orders per customer, updated instantly whenever a new order arrives. Because the current answer is already stored, looking it up is fast, without recalculating from all the history each time.

How it stays fresh

A small set of building blocks covers most everyday needs.

flowchart LR
  A["Raw orders stream"] --> B["Filter: remove test orders"]
  B --> C["Join: add customer details"]
  C --> D["Window and aggregate: orders per five minutes"]
  D --> E["Result topic for dashboards"]
        

Real questions usually need data from more than one place. Joins let ksqlDB combine them.

Stream with table: enriching events

The most common join adds background details to each event. For example, an order event carries only a customer number. By joining the order stream with a table of customer details, each order can be enriched with the customer’s name and city. For every arriving order, ksqlDB looks up the current row of the customer table and attaches it.

Stream with stream: matching events in time

Sometimes two streams of events must be matched with each other, such as an order event and its payment event. Because both streams never end, ksqlDB matches events that arrive within a chosen time window of each other, for example a payment within one hour of the order. Events that do not find a partner within the window are not matched.

Table with table: combining states

Two tables can also be joined, giving a combined view of the latest state from each, such as merging a table of account details with a table of account limits.

Since streams never end, questions like “how many?” only make sense over a period of time. Windows provide those periods.

Three common kinds of windows

flowchart LR
  subgraph Tumbling["Tumbling windows"]
    T1["0-5 min"] --> T2["5-10 min"] --> T3["10-15 min"]
  end
  subgraph Hopping["Hopping windows of 5 min every 1 min"]
    H1["0-5 min"] --> H2["1-6 min"] --> H3["2-7 min"]
  end
  subgraph Session["Session windows"]
    S1["Burst of activity"] --> S2["Quiet gap"] --> S3["New burst"]
  end
        

Late events

Events do not always arrive in the exact order of their times. A phone that was offline may send yesterday’s click today. ksqlDB lets you set a grace period, during which late events are still added to the right window. After the grace period, the window is closed, and very late events are dropped or ignored for that window.

A peek at the architecture helps you understand its strengths and limits.

The main parts

flowchart TD
  U["User: command line, API, or client library"] --> S1["ksqlDB Server 1"]
  U --> S2["ksqlDB Server 2"]
  S1 --> KS1["Kafka Streams engine and local state"]
  S2 --> KS2["Kafka Streams engine and local state"]
  KS1 --> K["Kafka Cluster: topics, changelogs, command topic"]
  KS2 --> K
        

Because ksqlDB stands on Kafka’s shoulders, it inherits Kafka’s habits of sharing work and healing itself.

Scaling by adding servers

Each ksqlDB query reads from partitions of Kafka topics. When you add more servers to a cluster, the partitions are spread among them, using the same idea as consumer groups. More servers mean more parallel work. As with any consumer group, the number of partitions caps how far a single query can be spread.

Surviving failures

If a server fails, the partitions it was handling are given to the surviving servers. The new owner needs the state that the failed server held, so it rebuilds it from the changelog topic in Kafka, or takes over from a standby copy if standby replicas are configured. During this rebuild, results for those partitions may be briefly delayed. Because inputs and changelogs are in replicated Kafka topics, no data is lost.

You can use ksqlDB in a exploratory way or in a fixed, production way.

ksqlDB shines when the need is a clear, SQL-shaped transformation of Kafka events.

ksqlDB is not the only way to process Kafka events. Choosing well means knowing the alternatives.

Choosing between them

Pick ksqlDB when your team knows SQL, the transformations are clear and standard, and you want results quickly. Pick Kafka Streams or your own code when the logic is complicated and belongs inside a service. Consider other stream engines when you need a very rich ecosystem or you already run them. It is also wise to check the current direction and support of each project, since the stream-processing landscape evolves, and vendors may emphasise different tools over time.

ksqlDB is a bargain between simplicity and flexibility.

A ksqlDB server can read and write your most important topics, so it deserves careful protection and watching.

Security

The ksqlDB server connects to Kafka with its own identity, and Kafka’s access rules decide which topics it may read and write. Give it only the topics it needs. Users and applications connect to the server itself, which can require encrypted connections and proof of identity, and can apply its own rules about who may run which statements. Because a single statement can create a query that reads a sensitive topic and copies it elsewhere, control who is allowed to submit statements, and review what queries exist.

Monitoring

Because each persistent query is a consumer group under the hood, you can watch its lag like any other. Also watch the health and state of each query, the processing rate, the amount of state on disk, the time spent restoring after failures, and errors from bad or unexpected data. A query that has quietly stopped is a common source of silent problems, so alert on query state, not only on servers being up.

A few habits keep ksqlDB projects healthy.

Best practices at a glance

Let us see how an online shop might use ksqlDB step by step.

flowchart LR
  O["orders stream"] --> F["Filter real orders"]
  F --> J["Join with customers table"]
  C["customers table"] --> J
  J --> W["Five-minute sales per city"]
  W --> PQ["Pull query: latest total for a city"]
  W --> SQ["Push query: live sales feed"]
        

It is a server that lets you process Kafka events using SQL-like statements, creating continuously running queries, streams, tables, and materialised views on top of Kafka topics.

No. It runs on top of Kafka and uses Kafka topics for both input and output, and for storing its state changes.

A stream is a sequence of events, each a new fact. A table holds the latest value for each key. A stream answers “what happened?” and a table answers “what is true now?”

A push query keeps sending you results as they happen. A pull query returns one answer from the current state of a table and finishes.

For many tasks you do not. Statements are written in a SQL-like language. For complex custom logic or for building applications around it, programming skills still help.

Yes. Each persistent query is run by a Kafka Streams engine inside the ksqlDB server, which is why it shares Kafka Streams’ scaling and fault-tolerance model.

Kafka rebalances the partitions to the remaining servers, and state is rebuilt from changelog topics or taken over from standby replicas, then processing continues.

No. It is designed for continuous processing and current-state lookups, not for large historical analysis. Use a warehouse for that and let ksqlDB feed it.

It is a table that ksqlDB keeps up to date automatically as new events arrive, so that the latest answer can be looked up instantly.

Choose ksqlDB when your team knows SQL and the transformations are standard. Choose Kafka Streams, custom code, or another engine when your logic is complex or your organisation already uses that engine. Also check the current roadmap and licence terms for your situation.

We began with a post office where a manager can either hire engineers to build machinery for every question or type the question into a control panel that rearranges the belts. ksqlDB is that control panel for Kafka. It lets you describe streams and tables, filter, transform, join, aggregate, and window events using SQL-like statements, and it runs those queries continuously on servers built on Kafka Streams. Persistent queries build pipelines, push queries deliver live feeds, and pull queries answer quick lookups against materialised views that stay fresh with every new event. Kafka provides the storage, scaling, and safety, and ksqlDB provides speed of development and a familiar language. In return, you accept less flexibility than custom code, a server cluster to run, state that needs disk and recovery planning, and a need to review its licence and future direction. Used for the right jobs, ksqlDB turns Kafka from a store of raw events into a source of live, ready-to-use answers.