Cassandra Data Model – ALLOW FILTERING vs NOT

I have a toy Cassandra cluster running on some RaspberryPis at home. I am currently logging CryptoCoin data to it in hopes of learning more about Cassandra and as well as some other things along the way.

My question here today is to find out if I am structuring my schema correctly on this one table.

The table doesn't have many fields, the primary keys are the name field and the timestamp field. I want to query the last N hours of data (data is logged every minute) from all coins. If I use a simple WHERE clause I get the 'ALLOW FILTERING' warning. I understand why it happens but am struggling to understand the correct path forward to ensure a scale-able solution. Right now the table only has about 320k records and I can use ALLOW FILTERING with no problem, but I realize this might not always be the case.

I setup a test to see how long it took to run two different query methods. The ALLOW FILTERING method currently is the fastest, but is it likely to stay that way? This is where I am deficient in knowledge.

I had an idea to add another field that would be the day of the week, and maybe a month field as well. The thought was this might allow for more filtering in a query so I don't have to iterate through all the coins like I am doing below, but I don't know if this is a good idea or not. If I do this, do I make them a PrimaryKey or not? Think this is where I am most confused with Cassandra, but not entirely; maybe just enough to be unconfident.

CQL Table Description:

CREATE TABLE cryptocoindb.worldcoinindex (
    name text,
    timestamp int,
    label text,
    price_btc double,
    price_cny double,
    price_eur double,
    price_gbp double,
    price_rur double,
    price_usd double,
    volume_24h double,
    PRIMARY KEY (name, timestamp)
) WITH CLUSTERING ORDER BY (timestamp ASC)
    AND bloom_filter_fp_chance = 0.01
    AND caching = {'keys': 'ALL', 'rows_per_partition': 'NONE'}
    AND comment = ''
    AND compaction = {'class': 'org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy', 'max_threshold': '32', 'min_threshold': '4'}
    AND compression = {'chunk_length_in_kb': '64', 'class': 'org.apache.cassandra.io.compress.LZ4Compressor'}
    AND crc_check_chance = 1.0
    AND dclocal_read_repair_chance = 0.1
    AND default_time_to_live = 0
    AND gc_grace_seconds = 864000
    AND max_index_interval = 2048
    AND memtable_flush_period_in_ms = 0
    AND min_index_interval = 128
    AND read_repair_chance = 0.0
    AND speculative_retry = '99PERCENTILE';

Code in Python:

# First method using ALLOW FILTERING:
startTime = time.time()
oneDaySec = 60*60*24
prior24hr = int(time.time()-oneDaySec)

query = "SELECT * FROM {}.{} WHERE timestamp > {} ALLOW FILTERING;".format(CASSANDRA_DB, CASSANDRA_TABLE, prior24hr)

rslt = session.execute(query, timeout=None)
worldcoinindex = rslt._current_rows
elapseTime = time.time()-startTime

print("Elapsed Time for this method: {}".format(elapseTime))

Elapsed Time for this method: 0.6223547458648682

# Second method using multiple queries...

startTime = time.time()

# I get the unique coin names here.
qryGetCoinList = "SELECT DISTINCT name FROM {}.{};".format(CASSANDRA_DB, CASSANDRA_TABLE)
rslt = session.execute(qryGetCoinList, timeout=None)
rsltGetCoinList = rslt._current_rows
rsltGetCoinList = rsltGetCoinList.name.tolist()

oneDaySec = 60*60*24
prior24hr = int(time.time()-oneDaySec)

# This iterates over the unique coin names and queries 
# the last 24 hrs worth of data per coin.
# NOTE: There are 518 unique coins.  

rsltTodayPrices = pd.DataFrame()
for coin in rsltGetCoinList:

    qryTodayPrices = """
                    SELECT * FROM {}.{} 
                    WHERE name = '{}' AND timestamp > {};
                    """.format(CASSANDRA_DB, 
                               CASSANDRA_TABLE, 
                               coin, 
                               prior24hr)
    rslt = session.execute(qryTodayPrices, timeout=None)
    TodayPrices = rslt._current_rows
    rsltTodayPrices.append(TodayPrices)

elapseTime = time.time()-startTime
print("Elapsed Time for this method: {}".format(elapseTime))

Elapsed Time for this method: 1.4576539993286133

Thank you!