On Wed, Feb 8, 2012 at 6:05 AM, aaron morton <firstname.lastname@example.org> wrote:
I would still try to do it without super columns. The common belief is they are about 10% slower, and they are a lot clunkier. There are some query and delete cases where they do things composite columns cannot, but in general I try to model things without using them first.None of those jump out at me as horrible for my case. If I modelled with Super Columns I would have less than 10,000 Super Columns with an average of 50 columns - big but no insane ?
pycassa has support for chunking requests to the serverBecause of request overhead ? I'm currently using the batch interface of pycassa to do bulk reads. Is the same problem going to bite me if I have many clients reading (using bulk reads) ? In production we will have ~50 clients.
It's because each row requested becomes a read task on the server and is placed into the read thread pool. There are only 32 (default) read thread in the pool. If one query comes along and requests 100 rows, it places 100 tasks in the thread pool where only 32 can be processed at a time. Some will back up as pending tasks and eventually be processed. If row reads reads take 1ms (just to pick a number, may be better) to read 100 rows we are talking about 3 or 4ms for that query. During that time any read requests received will have to wait for read threads.To that client this is excellent, it's has a high row throughput. To the other clients this is not, overall query throughput will drop. More is not always better. Note that as the number of nodes increases and this effect is may be reduced as reading 100 rows may result in the coordinator sending 25 row requests to 4 nodes.And there is also overhead involved in very big requests, see…
CheersOn 7/02/2012, at 2:28 PM, Franc Carter wrote:On Tue, Feb 7, 2012 at 6:39 AM, aaron morton <email@example.com> wrote:
Sounds like a good start. Super columns are not a great fit for modeling time series data for a few reasons, here is one http://wiki.apache.org/cassandra/CassandraLimitations
None of those jump out at me as horrible for my case. If I modelled with Super Columns I would have less than 10,000 Super Columns with an average of 50 columns - big but no insane ?
It's also a good idea to partition time series data so that the rows do not grow too big. You can have 2 billion columns in a row, but big rows have operational down sides.You could go with either:rows: <entity_id:date>column: <property_name>Which would mean each time your query for a date range you need to query multiple rows. But it is possible to get a range of columns / properties.Orrows: <entity_id:time_partition>column: <date:property_name>
That's an interesting idea - I'll talk to the data experts to see if we have a sensible range.
Where time_partition is something that makes sense in your problem domain, e.g. a calendar month. If you often query for days in a month you can then get all the columns for the days you are interested in (using a column range). If you only want to get a sub set of the entity properties you will need to get them all and filter them client side, depending on the number and size of the properties this may be more efficient than multiple calls.
I'm find with doing work on the client side - I have a bias in that direction as it tends to scale better.
One word of warning, avoid sending read requests for lots (i.e. 100's) of rows at once it will reduce overall query throughput. Some clients like pycassa take care of this for you.
Because of request overhead ? I'm currently using the batch interface of pycassa to do bulk reads. Is the same problem going to bite me if I have many clients reading (using bulk reads) ? In production we will have ~50 clients.
thanksGood luck.On 5/02/2012, at 12:12 AM, Franc Carter wrote:Hi,I'm pretty new to Cassandra and am currently doing a proof of concept, and thought it would be a good idea to ask if my data model is sane . . .The data I have, and need to query, is reasonably simple. It consists of about 10 million entities, each of which have a set of key/value properties for each day for about 10 years. The number of keys is in the 50-100 range and there will be a lot of overlap for keys in <entity,days>The queries I need to make are for sets of key/value properties for an entity on a day, e.g key1,keys2,key3 for 10 entities on 20 days. The number of entities and/or days in the query could be either very small or very large.I've modeled this with a simple column family for the keys with the row key being the concatenation of the entity and date. My first go, used only the entity as the row key and then used a supercolumn for each date. I decided against this mostly because it seemed more complex for a gain I didn't really understand.Does this seem sensible ?thanks
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