Apache Kafka is an open-source distributed event streaming platform, used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. For the key concepts of kafka, please refer to [kafka documentation](https://kafka.apache.org/documentation/#gettingStarted).
### kafka topic
Messages in Kafka are organized by topics. A topic may have one or more partitions. We can manage kafka topics through `kafka-topics`.
We can write data into TDengine via SQL or Schemaless. For more information, please refer to [Insert Using SQL](/develop/insert-data/sql-writing/) or [High Performance Writing](/develop/insert-data/high-volume/) or [Schemaless Writing](/reference/schemaless/).
For python kafka client, please refer to [kafka client](https://cwiki.apache.org/confluence/display/KAFKA/Clients#Clients-Python). In this document, we use [kafka-python](http://github.com/dpkp/kafka-python).
### consume from Kafka
The simple way to consume messages from Kafka is to read messages one by one. The demo is as follows:
```
from kafka import KafkaConsumer
consumer = KafkaConsumer('my_favorite_topic')
for msg in consumer:
print (msg)
```
For higher performance, we can consume message from kafka in batch. The demo is as follows:
For more higher performance we can process data from kafka in multi-thread. We can use python's ThreadPoolExecutor to achieve multithreading. The demo is as follows:
```
from concurrent.futures import ThreadPoolExecutor, Future
pool = ThreadPoolExecutor(max_workers=10)
pool.submit(...)
```
### multi-process
For more higher performance, sometimes we use multiprocessing. In this case, the number of Kafka Consumers should not be greater than the number of Kafka Topic Partitions. The demo is as follows:
```
from multiprocessing import Process
ps = []
for i in range(5):
p = Process(target=Consumer().consume())
p.start()
ps.append(p)
for p in ps:
p.join()
```
In addition to python's built-in multithreading and multiprocessing library, we can also use the third-party library gunicorn.
You can also download zip files from [GitHub](https://github.com/taosdata/grafanaplugin/releases/tag/latest) or [Grafana](https://grafana.com/grafana/plugins/tdengine-datasource/?tab=installation) and install manually. The commands are as follows:
