Thanks Duy Hai for sharing the details. I have a doubt. If for some reason
there is a Network Partition or more than 2 Node failure serving the same
partition/load and you ended up writing hinted hand-off.
Is there a possibility of a data loss? If yes, how do we avoid that?
Regards,
Jagan
---- On Sat, 22 Feb 2014 22:48:19 +0530 DuyHai Doan
<doanduy...@gmail.com> wrote ----
Jagan
Few time ago I dealed with a similar queuing design for one customer.
If you never delete messages in the queue, then it is possible to use wide
rows with bucketing and increasing monotonic column name to store messages.
CREATE TABLE read_only_queue (
bucket_number int,
insertion_time timeuuid,
message text,
PRIMARY KEY(bucket_number,insertion_time)
);
Let's say that you allow only 100 000 messages per partition (physical row)
to avoid too wide rows, then inserting/reading from the table read_only_queue
is easy;
For message producer :
1) Start at bucket_number = 1
2) Insert messages with column name = generated timeUUID with micro-second
precision (depending on whether the insertion rate is high or not)
3) If message count = 100 000, increment bucket_number by one and go to 2)
For message reader:
1) Start at bucket_number = 1
2) Read messages by slice of N, save the insertion_time of the last read
message
3) Use the saved insertion_time to perform next slice query
4) If read messages count = 100 000, increment bucket_number and go to 2).
Keep the insertion_time, do not reset it since his value is increasing
monotonically
For multiple and concurrent producers & writers, there is a trick. Let's
assume you have P concurrent producers and C concurrent consumers.
Assign a numerical ID for each producer and consumer. First producer ID =
1... last producer ID = P. Same for consumers.
- re-use the above algorithm
- each producer/consumer start at bucket_number = his ID
- at the end of the row,
- next bucket_number = current bucker_number + P for producers
- next bucket_number = current bucker_number + C for consumers
The last thing to take care of is compaction configuration to reduce the
number of SSTables on disk.
If you achieve to get rid of accumulation effects, e.g reading rate is faster
than writing rate, the message are likely to be consumed while it's still in
memory (in memtable) at server side. In this particular case, you can optimize
further by deactivating compaction for the table.
Regards
Duy Hai
On Sat, Feb 22, 2014 at 5:56 PM, Jagan Ranganathan <ja...@zohocorp.com>
wrote:
Hi,
Thanks for the pointer.
Following are some options given there,
If you know where your live data begins, hint Cassandra with a start
column, to reduce the scan times and the amount of tombstones to collect.
A broker will usually have some notion of what’s next in the sequence and
thus be able to do much more targeted queries, down to a single record if the
storage strategy were to choose monotonic sequence numbers.
We need to do is have some intelligence in using the system and avoid
tombstones either use the pointed Column Name or use proper start column if
slice query is used.
Is that right or I am missing something here?
Regards,
Jagan
---- On Sat, 22 Feb 2014 20:55:39 +0530 DuyHai Doan<doanduy...@gmail.com>
wrote ----
Jagan
Queue-like data structures are known to be one of the worst anti patterns
for Cassandra:
http://www.datastax.com/dev/blog/cassandra-anti-patterns-queues-and-queue-like-datasets
On Sat, Feb 22, 2014 at 4:03 PM, Jagan Ranganathan <ja...@zohocorp.com>
wrote:
Hi,
I need to decouple some of the work being processed from the user thread to
provide better user experience. For that I need a queuing system with the
following needs,
High Availability
No Data Loss
Better Performance.
Following are some libraries that were considered along with the limitation I
see,
Redis - Data Loss
ZooKeeper - Not advised for Queue system.
TokyoCabinet/SQLite/LevelDB - of this Level DB seem to be performing better.
With replication requirement, I probably have to look at Apache
ActiveMQ+LevelDB.
After checking on the third option above, I kind of wonder if Cassandra with
Leveled Compaction offer a similar system. Do you see any issues in such a
usage or is there other better solutions available.
Will be great to get insights on this.
Regards,
Jagan
