From e4bf0d4598e95e5cf8ed2f054f7d2feb97675853 Mon Sep 17 00:00:00 2001
From: HalfSweet <60973476+HalfSweet@users.noreply.github.com>
Date: Tue, 27 Feb 2024 13:01:19 +0000
Subject: [PATCH] docs: Update code block syntax in docs/index.rst

---
 docs/index.rst | 112 ++++++++++++++++++++++++++++++-------------------
 1 file changed, 68 insertions(+), 44 deletions(-)

diff --git a/docs/index.rst b/docs/index.rst
index 91e5086cc..71ae71416 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -31,7 +31,9 @@ failures.  See `Compatibility <compatibility.html>`_ for more details.
 Please note that the master branch may contain unreleased features. For release
 documentation, please see readthedocs and/or python's inline help.
 
->>> pip install kafka-python
+.. code:: bash
+
+    pip install kafka-python
 
 
 KafkaConsumer
@@ -47,28 +49,36 @@ See `KafkaConsumer <apidoc/KafkaConsumer.html>`_ for API and configuration detai
 The consumer iterator returns ConsumerRecords, which are simple namedtuples
 that expose basic message attributes: topic, partition, offset, key, and value:
 
->>> from kafka import KafkaConsumer
->>> consumer = KafkaConsumer('my_favorite_topic')
->>> for msg in consumer:
-...     print (msg)
+.. code:: python
+
+    from kafka import KafkaConsumer
+    consumer = KafkaConsumer('my_favorite_topic')
+    for msg in consumer:
+        print (msg)
+
+.. code:: python
 
->>> # join a consumer group for dynamic partition assignment and offset commits
->>> from kafka import KafkaConsumer
->>> consumer = KafkaConsumer('my_favorite_topic', group_id='my_favorite_group')
->>> for msg in consumer:
-...     print (msg)
+    # join a consumer group for dynamic partition assignment and offset commits
+    from kafka import KafkaConsumer
+    consumer = KafkaConsumer('my_favorite_topic', group_id='my_favorite_group')
+    for msg in consumer:
+        print (msg)
 
->>> # manually assign the partition list for the consumer
->>> from kafka import TopicPartition
->>> consumer = KafkaConsumer(bootstrap_servers='localhost:1234')
->>> consumer.assign([TopicPartition('foobar', 2)])
->>> msg = next(consumer)
+.. code:: python
 
->>> # Deserialize msgpack-encoded values
->>> consumer = KafkaConsumer(value_deserializer=msgpack.loads)
->>> consumer.subscribe(['msgpackfoo'])
->>> for msg in consumer:
-...     assert isinstance(msg.value, dict)
+    # manually assign the partition list for the consumer
+    from kafka import TopicPartition
+    consumer = KafkaConsumer(bootstrap_servers='localhost:1234')
+    consumer.assign([TopicPartition('foobar', 2)])
+    msg = next(consumer)
+
+.. code:: python
+
+    # Deserialize msgpack-encoded values
+    consumer = KafkaConsumer(value_deserializer=msgpack.loads)
+    consumer.subscribe(['msgpackfoo'])
+    for msg in consumer:
+        assert isinstance(msg.value, dict)
 
 
 KafkaProducer
@@ -78,36 +88,50 @@ KafkaProducer
 The class is intended to operate as similarly as possible to the official java
 client. See `KafkaProducer <apidoc/KafkaProducer.html>`_ for more details.
 
->>> from kafka import KafkaProducer
->>> producer = KafkaProducer(bootstrap_servers='localhost:1234')
->>> for _ in range(100):
-...     producer.send('foobar', b'some_message_bytes')
+.. code:: python
+
+    from kafka import KafkaProducer
+    producer = KafkaProducer(bootstrap_servers='localhost:1234')
+    for _ in range(100):
+        producer.send('foobar', b'some_message_bytes')
+
+.. code:: python
+
+    # Block until a single message is sent (or timeout)
+    future = producer.send('foobar', b'another_message')
+    result = future.get(timeout=60)
+
+.. code:: python
+
+    # Block until all pending messages are at least put on the network
+    # NOTE: This does not guarantee delivery or success! It is really
+    # only useful if you configure internal batching using linger_ms
+    producer.flush()
+
+.. code:: python
+
+    # Use a key for hashed-partitioning
+    producer.send('foobar', key=b'foo', value=b'bar')
 
->>> # Block until a single message is sent (or timeout)
->>> future = producer.send('foobar', b'another_message')
->>> result = future.get(timeout=60)
+.. code:: python
 
->>> # Block until all pending messages are at least put on the network
->>> # NOTE: This does not guarantee delivery or success! It is really
->>> # only useful if you configure internal batching using linger_ms
->>> producer.flush()
+    # Serialize json messages
+    import json
+    producer = KafkaProducer(value_serializer=lambda v: json.dumps(v).encode('utf-8'))
+    producer.send('fizzbuzz', {'foo': 'bar'})
 
->>> # Use a key for hashed-partitioning
->>> producer.send('foobar', key=b'foo', value=b'bar')
+.. code:: python
 
->>> # Serialize json messages
->>> import json
->>> producer = KafkaProducer(value_serializer=lambda v: json.dumps(v).encode('utf-8'))
->>> producer.send('fizzbuzz', {'foo': 'bar'})
+    # Serialize string keys
+    producer = KafkaProducer(key_serializer=str.encode)
+    producer.send('flipflap', key='ping', value=b'1234')
 
->>> # Serialize string keys
->>> producer = KafkaProducer(key_serializer=str.encode)
->>> producer.send('flipflap', key='ping', value=b'1234')
+.. code:: python
 
->>> # Compress messages
->>> producer = KafkaProducer(compression_type='gzip')
->>> for i in range(1000):
-...     producer.send('foobar', b'msg %d' % i)
+    # Compress messages
+    producer = KafkaProducer(compression_type='gzip')
+    for i in range(1000):
+        producer.send('foobar', b'msg %d' % i)
 
 
 Thread safety