Thank you all.
I found doc in datastax and it said the compression is default to enabled
and set it as LZ4Compressor.
Dipan Shah 於 2019年12月31日 週二 下午5:34寫道:
> Hello lampahome,
>
> Data will be compressed but you will also have to account for the
> replication factor that you will be using.
>
> Thanks,
>
> Dipan Shah
>
>
The key factor about efficiency is replication factor. Are there other
factors?
If I use var a as primary key and var b as second key, and a and b are 16
bytes and 8 bytes.
And other data are 32 bytes.
In one row, I have a+b+data = 16+8+32 = 56 bytes.
If I have 100,000 rows to store in cassandra, will it occupy space
56x10 bytes in my disk? Or data will be compressed?
I read some difference between nosql and sql, and one obvious differences
is nosql supporting schemaless.
But I try it in cassandra and get result not like that.
Ex:
cqlsh:key> Create table if not exists yo (blk bigint primary key, count
int);
cqlsh:key> insert into yo (blk, count, test) values (
I use cassandra-python driver and try to be familiar with preparedstatement
to improve performance.
I saw doc on datastax about it, but it doesn't describe detaily.
Can anyone explain what does it prepare what? Will that help performance?
thx
As title, I want to utilize cassandra's advantages in maximum but I don't
know how.
So far, I know I can improve performance by execute_async and
batchstatement.
When I want more nodes scalability, just add server and modify some config
files.
Are there ways to help me use cassandra-python bette
Jon Haddad 於 2019年12月12日 週四 上午12:42寫道:
> I'm not sure how you're measuring this - could you share your benchmarking
> code?
>
>> s the details of theri?
>>
>
start = time.time()
for i in range(40960):
prep = session.prepare(query, (args))
session.execute(prep) # or session.execute_async(p
I tried to execute async by batch in python-driver. But I don't know how to
check query executing correctly.
Code is like below:
B = BatchStatement()
for x in xxx:
B.add(query, (args))
res = session.execute_async(B)
B.clear() # for reusing
r = res.result()
## Then how to know my query works co
Jordan West 於 2019年12月11日 週三 下午4:34寫道:
> Hi,
>
> Have you tried batching calls to execute_async with periodic blocking for
> the batch’s responses?
>
Can you give me some keywords about calling execute_async batch?
PS: I use python version.
I submit 1 row for 40960 times by session.execute() and
session.execute_async()
I found total time of execute() is always fast than execute_async
Does that make sense? Or I miss the details of theri?
10 matches
Mail list logo