kashpy 0.0.3

A Kafka Shell based on Python

kash.py

kash.py is a Kafka shell based on Python, or, in other words, a Python-based client library for Kafka based on confluent-kafka-python by Magnus Edenhill, which is itself based on the native Kafka client library librdkafka by the same author.

The idea behind kash.py is to make it as easy as possible to interact with Kafka using Python, without having to know any of the implementation details of the underlying confluent-kafka-python module. To this end, not only are the functions/methods are simpler to use, but also all classes are converted to simple Python types like tuples and dictionaries. As a result, chores that took numerous of lines of boilerplate code before can be formulated as one-liners, both in interactive mode using the Python REPL, or within convenient scripts.

kash.py has been built for Kafka users of all kinds:

• For developers and devops engineers to view and manipulate Kafka topics using familiar shell syntax (you have ls, touch, rm, cp, cat, grep, wc etc.).
• For data scientists to bridge the gap between batch and stream processing, using functions to upload/download files to/from topics, and even functional abstractions a la Databricks/Apache Spark (there are various foldls, flatmaps and maps for you to explore).

kash.py supports Avro, Protobuf and JSONSchema, Confluent Cloud, Redpanda, etc...and it will give you Kafka superpowers of a kind you have never experienced before. Honestly :)

Check out the full kashpy package documentation if you are interested in seeing the entire functionality of kash.py.

Installation

Just write...

pip install kashpy

...and off you go.

Configuration

kash.py makes use of configuration files suffixed .conf which are being searched for in one of these two folders: clusters_unsecured or clusters_secured, starting 1) from the directory in the KASHPY_HOME environment variable, or, if that environment variable is not set, 2) from the current directory.

A barebones configuration file looks like this (including Schema Registry):

[kafka]
bootstrap.servers=localhost:9092

[schema_registry]
schema.registry.url=http://localhost:8081

You can also set some of the defaults of kash.py in the [kash] section like this:

[kash]
flush.num.messages=10000
flush.timeout=-1.0
retention.ms=-1
consume.timeout=1.0
auto.offset.reset=earliest
enable.auto.commit=true
session.timeout.ms=10000
progress.num.messages=1000
block.num.retries.int=50
block.interval=0.1

You can find an in-depth explanation of these settings in the kashpy package documentation, including example configuration files for connecting to Confluent Cloud etc.

Kafka Made Simple

For interactive use, e.g. using your local cluster configured in the file clusters_unsecured/local.conf, just do the following to list the topics:

$ python3
>>> from kashpy.kash import *
>>> c = Cluster("local")
>>> c.ls()
['__consumer_offsets', '_schemas']
>>>

Or, if you'd like to replicate the topic test holding 1000 messages from a Kafka cluster local on your local machine to a Kafka cluster ccloud on Confluent Cloud:

>>> c_local = Cluster("local")
>>> c_ccloud = Cluster("ccloud")
>>> c.cp(c_local, "test", c_ccloud, "test")
(1000, 1000)
>>>

In the following, we go a bit deeper in two short tutorials; the first demonstrating how kash.py helps you to fulfill tasks on a single cluster, and the second how to make use of your newly obtained Kafka superpowers across clusters.

Tutorial 1 (single-cluster)

This is the first tutorial, showcasing the single cluster capabilities of kash.py in interactive mode.

Let's start Python, import kash.py and create a Cluster object c:

$ python3
>>> from kashpy.kash import *
>>> c = Cluster("local")
>>>

List the topics on the cluster:

>>> c.ls()
['__consumer_offsets', '_schemas']
>>>

Create a new topic snacks:

>>> c.touch("snacks")
'snacks'
>>> 

List the topics on the cluster again:

>>> c.ls()
['__consumer_offsets', '_schemas', 'snacks']
>>>

Upload the following local file snacks.txt to the topic snacks (examples inspired by the great blog Kafka with AVRO vs., Kafka with Protobuf vs., Kafka with JSON Schema by Simon Aubury):

{"name": "cookie", "calories": 500.0, "colour": "brown"}
{"name": "cake", "calories": 260.0, "colour": "white"}
{"name": "timtam", "calories": 80.0, "colour": "chocolate"}

>>> c.cp("./snacks.txt", "snacks")
(3, 3)
>>>

Show the contents of topic snacks:

>>> c.cat("snacks")
{'headers': None, 'partition': 0, 'offset': 0, 'timestamp': (1, 1664989815680), 'key': None, 'value': '{"name": "cookie", "calories": 500.0, "colour": "brown"}'}
{'headers': None, 'partition': 0, 'offset': 1, 'timestamp': (1, 1664989815680), 'key': None, 'value': '{"name": "cake", "calories": 260.0, "colour": "white"}'}
{'headers': None, 'partition': 0, 'offset': 2, 'timestamp': (1, 1664989815680), 'key': None, 'value': '{"name": "timtam", "calories": 80.0, "colour": "chocolate"}'}
3
>>> 

Count the number of messages, words and bytes of the topic snacks:

>>> c.wc("snacks")
(3, 18, 169)
>>>

Find those messages whose values matches the regular expression .*cake.*:

>>> c.grep("snacks", ".*cake.*")
Found matching message on partition 0, offset 1.
([{'headers': None, 'partition': 0, 'offset': 1, 'timestamp': (1, 1664989815680), 'key': None, 'value': '{"name": "cake", "calories": 260.0, "colour": "white"}'}], 1, 3)
>>>

Create a new topic snacks_protobuf:

>>> c.touch("snacks_protobuf")
'snacks_protobuf'
>>>

Copy the topic snacks onto another topic snacks_protobuf using Protobuf (and storing the schema in the Schema Registry):

>>> c.cp("snacks", "snacks_protobuf", target_value_type="protobuf", target_value_schema='message Snack { required string name = 1; required float calories = 2; optional string colour = 3; }')
(3, 3)
>>>

Show the contents of topic snacks_protobuf (showing the values directly as bytes):

>>> c.cat("snacks_protobuf", value_type="bytes")
{'headers': None, 'partition': 0, 'offset': 0, 'timestamp': (1, 1664989815680), 'key': None, 'value': b'\x00\x00\x00\x00\x03\x00\n\x06cookie\x15\x00\x00\xfaC\x1a\x05brown'}
{'headers': None, 'partition': 0, 'offset': 1, 'timestamp': (1, 1664989815680), 'key': None, 'value': b'\x00\x00\x00\x00\x03\x00\n\x04cake\x15\x00\x00\x82C\x1a\x05white'}
{'headers': None, 'partition': 0, 'offset': 2, 'timestamp': (1, 1664989815680), 'key': None, 'value': b'\x00\x00\x00\x00\x03\x00\n\x06timtam\x15\x00\x00\xa0B\x1a\tchocolate'}
>>>

Show the contents of the topic snacks_protobuf again (decoding the values using Protobuf and the Schema Registry):

>>> c.cat("snacks_protobuf", value_type="protobuf")
{'headers': None, 'partition': 0, 'offset': 0, 'timestamp': (1, 1664989815680), 'key': None, 'value': {'name': 'cookie', 'calories': 500.0, 'colour': 'brown'}}
{'headers': None, 'partition': 0, 'offset': 1, 'timestamp': (1, 1664989815680), 'key': None, 'value': {'name': 'cake', 'calories': 260.0, 'colour': 'white'}}
{'headers': None, 'partition': 0, 'offset': 2, 'timestamp': (1, 1664989815680), 'key': None, 'value': {'name': 'timtam', 'calories': 80.0, 'colour': 'chocolate'}}
3
>>>

Get a diff of the two topics snacks and snacks_protobuf, comparing the dictionaries obtained by converting the string payload in snacks to Python dictionaries, and the Protobuf payload in snacks_protobuf to Python dictionaries as well:

>>> c.diff("snacks", "snacks_protobuf", value_type1="json", value_type2="protobuf")
([], 3, 3)
>>>

Now we are getting functional - using a foldl operation to sum up the calories of the messages in snacks:

>>> c.foldl("snacks", lambda acc, x: acc + x["value"]["calories"], 0, value_type="json")
(840.0, 3)
>>>

We can also use a flatmap operation to get a list of all messages in snacks where each message is duplicated:

>>> c.flatmap("snacks", lambda x: [x, x])
([{'headers': None, 'partition': 0, 'offset': 0, 'timestamp': (1, 1664989815680), 'key': None, 'value': '{"name": "cookie", "calories": 500.0, "colour": "brown"}'}, {'headers': None, 'partition': 0, 'offset': 0, 'timestamp': (1, 1664989815680), 'key': None, 'value': '{"name": "cookie", "calories": 500.0, "colour": "brown"}'}, {'headers': None, 'partition': 0, 'offset': 1, 'timestamp': (1, 1664989815680), 'key': None, 'value': '{"name": "cake", "calories": 260.0, "colour": "white"}'}, {'headers': None, 'partition': 0, 'offset': 1, 'timestamp': (1, 1664989815680), 'key': None, 'value': '{"name": "cake", "calories": 260.0, "colour": "white"}'}, {'headers': None, 'partition': 0, 'offset': 2, 'timestamp': (1, 1664989815680), 'key': None, 'value': '{"name": "timtam", "calories": 80.0, "colour": "chocolate"}'}, {'headers': None, 'partition': 0, 'offset': 2, 'timestamp': (1, 1664989815680), 'key': None, 'value': '{"name": "timtam", "calories": 80.0, "colour": "chocolate"}'}], 3)
>>>

Next, we use a map operation to add the suffix ish to all the colours:

>>> def map_function(x):
...   x["value"]["colour"] += "ish"
...   return x
... 
>>> c.map("snacks", map_function, value_type="json")
([{'headers': None, 'partition': 0, 'offset': 0, 'timestamp': (1, 1664989815680), 'key': None, 'value': {'name': 'cookie', 'calories': 500.0, 'colour': 'brownish'}}, {'headers': None, 'partition': 0, 'offset': 1, 'timestamp': (1, 1664989815680), 'key': None, 'value': {'name': 'cake', 'calories': 260.0, 'colour': 'whiteish'}}, {'headers': None, 'partition': 0, 'offset': 2, 'timestamp': (1, 1664989815680), 'key': None, 'value': {'name': 'timtam', 'calories': 80.0, 'colour': 'chocolateish'}}], 3)
>>>

And last, but not least, we copy the topic snacks back to a local file snacks1.txt while both duplicating the messages and adding the suffix ish to all the colours:

>>> def flatmap_function(x):
...   x["value"]["colour"] += "ish"
...   return [x, x]
... 
>>> c.flatmap_to_file("snacks", "./snacks1.txt", flatmap_function, value_type="json")
(3, 6)
>>>

The resulting file snacks1.txt looks like this:

{"name": "cookie", "calories": 500.0, "colour": "brownish"}
{"name": "cookie", "calories": 500.0, "colour": "brownish"}
{"name": "cake", "calories": 260.0, "colour": "whiteish"}
{"name": "cake", "calories": 260.0, "colour": "whiteish"}
{"name": "timtam", "calories": 80.0, "colour": "chocolateish"}
{"name": "timtam", "calories": 80.0, "colour": "chocolateish"}

Tutorial 2 (cross-cluster)

This is the second tutorial, showcasing the cross-cluster capabilities of kash.py in interactive mode.

Again, let's start Python, import kash.py and create a Cluster object c1:

$ python3
>>> from kashpy.kash import *
>>> c1 = Cluster("local")
>>>

Create a new topic snacks1 on cluster c1:

>>> c1.touch("snacks1")
'snacks1'
>>>

Upload the following local file snacks.txt to the topic snacks1 on cluster c1:

{"name": "cookie", "calories": 500.0, "colour": "brown"}
{"name": "cake", "calories": 260.0, "colour": "white"}
{"name": "timtam", "calories": 80.0, "colour": "chocolate"}

>>> c1.cp("./snacks.txt", "snacks1")
(3, 3)

Create a new Cluster object c2:

>>> c2 = Cluster("ccloud")
>>>

Create a new topic snacks2 on c2:

>>> c2.touch("snacks2")
'snacks2'
>>>

Copy the topic snacks1 from cluster c1 to topic snacks2 on cluster c2:

>>> cp(c1, "snacks1", c2, "snacks2")
(3, 3)
>>>

Get the diff from topic snacks1 on cluster c1 to a topic snacks2 on cluster c2:

>>> diff(c1, "snacks1", c2, "snacks2")
([], 3, 3)
>>>

Now let's venture into the cross-cluster functional programming domain... let's copy topic snacks1 on cluster c1 to a topic snacks2_duplicate on cluster c2, while duplicating each message using a flatmap operation:

>>> flatmap(c1, "snacks1", c2, "snacks2_duplicate", lambda x: [x, x])
(3, 6)
>>>

Next, we use a cross-cluster map operation to add the suffix ish to all the colours and produce the resulting messages to topic snacks2_ish on cluster c2:

>>> def map_function(x):
...   x["value"]["colour"] += "ish"
...   return x
... 
>>> map(c1, "snacks1", c2, "snacks2_ish", map_function, source_value_type="json")
(3, 3)
>>>

Now for the most advanced operation of the tutorials: A zip of topic snacks1 on cluster c1 and topic snacks2_ish on cluster c2 followed by a foldl accumulating pairs of those messages from both topics where the colour of the message from topic snacks2_ish ends with eish:

>>> def zip_foldl_function(acc, x1, x2):
...   return acc + [(x1, x2)] if x2["value"]["colour"].endswith("eish") else acc
... 
>>> zip_foldl(c1, "snacks1", c2, "snacks2_ish", zip_foldl_function, [], value_type2="json")
([({'headers': None, 'partition': 0, 'offset': 1, 'timestamp': (1, 1664989815680), 'key': None, 'value': b'{"name": "cake", "calories": 260.0, "colour": "white"}'}, {'headers': None, 'partition': 0, 'offset': 1, 'timestamp': (1, 1664989815680), 'key': None, 'value': {'name': 'cake', 'calories': 260.0, 'colour': 'whiteish'}}), ({'headers': None, 'partition': 0, 'offset': 2, 'timestamp': (1, 1664989815680), 'key': None, 'value': b'{"name": "timtam", "calories": 80.0, "colour": "chocolate"}'}, {'headers': None, 'partition': 0, 'offset': 2, 'timestamp': (1, 1664989815680), 'key': None, 'value': {'name': 'timtam', 'calories': 80.0, 'colour': 'chocolateish'}})], 3, 3)
>>>