> ## Documentation Index
> Fetch the complete documentation index at: https://upstash.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Features

### BITFIELD and BITFIELD\_RO

One particular case is represented by these two chained commands, which are
available as functions that return an instance of the `BITFIELD` and,
respectively, `BITFIELD_RO` classes. Use the `execute` function to run the
commands.

```python theme={"system"}
redis.bitfield("test_key") \
  .incrby(encoding="i8", offset=100, increment=100) \
  .overflow("SAT") \
  .incrby(encoding="i8", offset=100, increment=100) \
  .execute()

redis.bitfield_ro("test_key_2") \
  .get(encoding="u8", offset=0) \
  .get(encoding="u8", offset="#1") \
  .execute()
```

### Custom commands

If you want to run a command that hasn't been implemented, you can use the
`execute` function of your client instance and pass the command as a `list`.

```python theme={"system"}
redis.execute(["XLEN", "test_stream"])
```

# Encoding

Although Redis can store invalid JSON data, there might be problems with the
deserialization. To avoid this, the Upstash REST proxy is capable of encoding
the data as base64 on the server and then sending it to the client to be
decoded.

For very large data, this can add a few milliseconds in latency. So, if you're
sure that your data is valid JSON, you can set `rest_encoding` to `None`.

# Retry mechanism

upstash-redis has a fallback mechanism in case of network or API issues. By
default, if a request fails it'll retry once, 3 seconds after the error. If you
want to customize that, set `rest_retries` and `rest_retry_interval` (in
seconds).

# Pipelines & Transactions

If you want to submit commands in batches to reduce the number of roundtrips, you can utilize pipelining or
transactions. The difference between pipelines and transactions is that transactions are atomic: no other
command is executed during that transaction. In pipelines there is no such guarantee.

To use a pipeline, simply call the `pipeline` method:

```python theme={"system"}
pipeline = redis.pipeline()

pipeline.set("foo", 1)
pipeline.incr("foo")
pipeline.get("foo")

result = pipeline.exec()

print(result)
# prints [True, 2, '2']
```

For transaction, use `mutli`:

```python theme={"system"}
pipeline = redis.multi()

pipeline.set("foo", 1)
pipeline.incr("foo")
pipeline.get("foo")

result = pipeline.exec()

print(result)
# prints [True, 2, '2']
```

You can also chain the commands:

```python theme={"system"}
pipeline = redis.pipeline()

pipeline.set("foo", 1).incr("foo").get("foo")
result = pipeline.exec()

print(result)
# prints [True, 2, '2']
```

# Telemetry

This library sends anonymous telemetry data to help us improve your experience.
We collect the following:

* SDK version
* Platform (Vercel, AWS)
* Python Runtime version

You can opt out by passing `allow_telemetry=False` when initializing the Redis client:

```py theme={"system"}
redis = Redis(
  # ...,
  allow_telemetry=False,
)
```