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Query Kafka with SQL


This tutorial is mainly for Timeplus Proton users. For Timeplus Cloud users, please check the guide for connecting Timeplus with Confluent Cloud with web UI. SQL in this guide can be ran both in Timeplus Proton and Timeplus Cloud/Enterprise.

A docker-compose file is created to bundle proton image with Redpanda (as lightweight server with Kafka API), Redpanda Console, and owl-shop as sample live data.

  1. Download the docker-compose.yml and put into a new folder.
  2. Open a terminal and run docker compose up in this folder.
  3. Wait for few minutes to pull all required images and start the containers. Visit http://localhost:8080 to use Redpanda Console to explore the topics and live data.
  4. Use proton-client to run SQL to query such Kafka data: docker exec -it <folder>-proton-1 proton-client You can get the container name via docker ps
  5. Create an external stream to connect to a topic in the Kafka/Redpanda server and run SQL to filter or aggregate data.

Create an external stream

CREATE EXTERNAL STREAM frontend_events(raw string)
SETTINGS type='kafka',

Since Proton 1.3.24, you can also define multiple columns.

CREATE EXTERNAL STREAM frontend_events_json(
version int,
requestedUrl string,
method string,
correlationId string,
ipAddress string,
requestDuration int,
response string,
headers string
SETTINGS type='kafka',

Then select the columns directly, without JSON parsing, e.g. select method from frontend_events_json For nested data, you can select headers:referrer from frontend_events_json

Explore the data in Kafka

Then you can scan incoming events via

select * from frontend_events

There are about 10 rows in each second. Only one column raw with sample data as following:

"version": 0,
"requestedUrl": "",
"method": "PUT",
"correlationId": "0c7e970a-f65d-429a-9acf-6a136ce0a6ae",
"ipAddress": "",
"requestDuration": 678,
"response": { "size": 2232, "statusCode": 200 },
"headers": {
"accept": "*/*",
"accept-encoding": "gzip",
"cache-control": "max-age=0",
"origin": "",
"referrer": "",
"user-agent": "Opera/10.41 (Macintosh; U; Intel Mac OS X 10_9_8; en-US) Presto/2.10.292 Version/13.00"

Cancel the query by pressing Ctrl+C.

Get live count

select count() from frontend_events

This query will show latest count every 2 seconds, without rescanning older data. This is a good example of incremental computation in Proton.

Filter events by JSON attributes

select _tp_time, raw:ipAddress, raw:requestedUrl from frontend_events where raw:method='POST'

Once you start the query, any new event with method value as POST will be selected. raw:key is a shortcut to extract string value from the JSON document. It also supports nested structure, such as raw:headers.accept

Aggregate data every second

select window_start, raw:method, count() from tumble(frontend_events,now(),1s)
group by window_start, raw:method

Every second, it will show the aggregation result for the number of events per HTTP method.

Show a live ASCII bar chart

Combining the interesting bar function from ClickHouse, you can use the following streaming SQL to visualize the top 5 HTTP methods per your clickstream.

select raw:method, count() as cnt, bar(cnt, 0, 40,5) as bar from frontend_events
group by raw:method order by cnt desc limit 5 by emit_version()
│ DELETE │ 35 │ ████▍ │
│ POST │ 29 │ ███▋ │
│ GET │ 27 │ ███▍ │
│ HEAD │ 25 │ ███ │
│ PUT │ 22 │ ██▋ │


  • This is a global aggregation, emitting results every 2 seconds (configurable).
  • emit_version() function to show an auto-increasing number for each emit of streaming query result
  • limit 5 by emit_version() to get the first 5 rows with the same emit_version(). This is a special syntax in Proton. The regular limit 5 will cancel the entire SQL once 5 results are returned. But in this streaming SQL, we'd like to show 5 rows for each emit interval.

Create a materialized view to save notable events in Proton

With External Stream, you can query data in Kafka without saving the data in Proton. You can create a materialized view to selectively save some events, so that even the data in Kafka is removed, they remain available in Timeplus.

For example, the following SQL will create a materialized view to save those broken links with parsed attributes from JSON, such as URL, method, referrer.

create materialized view mv_broken_links as
select raw:requestedUrl as url,raw:method as method, raw:ipAddress as ip,
raw:response.statusCode as statusCode, domain(raw:headers.referrer) as referrer
from frontend_events where raw:response.statusCode<>'200';

Later on you can directly query on the materialized view:

-- streaming query
select * from mv_broken_links;

-- historical query
select method, count() as cnt, bar(cnt,0,40,5) as bar from table(mv_broken_links)
group by method order by cnt desc;
│ GET │ 25 │ ███ │
│ DELETE │ 20 │ ██▌ │
│ HEAD │ 17 │ ██ │
│ POST │ 17 │ ██ │
│ PUT │ 17 │ ██ │
│ PATCH │ 17 │ ██ │