How to set/get pandas.DataFrame to/from Redis?

Question

After setting a DataFrame to redis, then getting it back, redis returns a string and I can’t figure out a way to convert this str to a DataFrame.

How can I do these two appropriately?

Asked By: Alex Luya

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Source

Answer 1

set:

redisConn.set("key", df.to_msgpack(compress='zlib'))

get:

pd.read_msgpack(redisConn.get("key"))

Answered By: Alex Luya

Answer 2

I couldn’t use msgpack because of Decimal objects in my dataframe. Instead I combined pickle and zlib together like this, assuming a dataframe df and a local instance of Redis:

import pickle
import redis
import zlib

EXPIRATION_SECONDS = 600

r = redis.StrictRedis(host='localhost', port=6379, db=0)

# Set
r.setex("key", EXPIRATION_SECONDS, zlib.compress( pickle.dumps(df)))

# Get
rehydrated_df = pickle.loads(zlib.decompress(r.get("key")))

There isn’t anything dataframe specific about this.

Caveats

the other answer using msgpack is better — use it if it works for you
pickling can be dangerous — your Redis server needs to be secure or you’re asking for trouble

Answered By: Mark Chackerian

Answer 3

For caching a dataframe use this.

import pyarrow as pa

def cache_df(alias,df):

    pool = redis.ConnectionPool(host='host', port='port', db='db')
    cur = redis.Redis(connection_pool=pool)
    context = pa.default_serialization_context()
    df_compressed =  context.serialize(df).to_buffer().to_pybytes()

    res = cur.set(alias,df_compressed)
    if res == True:
        print('df cached')

For fetching the cached dataframe use this.

def get_cached_df(alias):

    pool = redis.ConnectionPool(host='host',port='port', db='db') 
    cur = redis.Redis(connection_pool=pool)
    context = pa.default_serialization_context()
    all_keys = [key.decode("utf-8") for key in cur.keys()]

    if alias in all_keys:   
        result = cur.get(alias)

        dataframe = pd.DataFrame.from_dict(context.deserialize(result))

        return dataframe

    return None

Answered By: Lucky M.E.

Answer 4

import pandas as pd
df = pd.DataFrame([1,2])
redis.setex('df',100,df.to_json())
df = redis.get('df')
df = pd.read_json(df)

Answered By: Quantum Dreamer

Answer 5

to_msgpack is not available at the last versions of Pandas.

import redis
import pandas as pd

# Create a redis client
redisClient = redis.StrictRedis(host='localhost', port=6379, db=0)
# Create un dataframe
dd = {'ID': ['H576','H577','H578','H600', 'H700'],
  'CD': ['AAAAAAA', 'BBBBB', 'CCCCCC','DDDDDD', 'EEEEEEE']}
df = pd.DataFrame(dd)
data = df.to_json()
redisClient.set('dd', data)
# Retrieve the data
blob = redisClient.get('dd')
df_from_redis = pd.read_json(blob)
df_from_redis.head()

output

Answered By: ijasanchez

Answer 6

It’s 2021, which means df.to_msgpack() is deprecated AND pyarrow has deprecated their custom serialization functionality as of pyarrow 2.0. (see the "Arbitrary Object Serialization" section on pyarrow’s serialization page

That leaves good & trusty msgpack to serialize objects such that they can be pushed/stored into redis.

import msgpack
import redis 

# ...Writing to redis (already have data & a redis connection client)
redis_client.set('data_key_name', msgpack.packb(data))

# ...Retrieving from redis
retrieved_data = msgpack.unpackb(redis_client.get('data_key_name'))

Answered By: user1243797

How to set/get pandas.DataFrame to/from Redis?

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