Duncan,
Thanks for that feedback. I'll give a bit more info and then ask some more
questions.
*Our Goal*: Not to produce the fastest read but show horizontal scaling.
*Test procedure*:
* Inserted 54M rows where one third of that represents a unique key, 18M
keys. End result given our schema is the 54M rows becomes 72M rows in the
column family as the control query load to use.
* have a client that queries 100k records in configurable batches, set to
1k. And then it does 100 reps of queries. It doesn't do the same keys for
each rep, it uses an offset and then it increases the keys to query.
* We can adjust the hit rate, i.e. how many of the keys will be found but
have been focused on 100% hit rate
* we run the query where multiple clients can be spawned to do the same
query cycle 100k keys but the offset is not different so each client will
query the same keys.
* We thought we should manually compact the tables down to 1 sstable on a
given node for consistent results across different cluster sizes
* We had set replication factor to 1 originally to not complicate things or
impact initial write times even. We would assess rf later was our thought.
Since we changed the keys getting queried it would have to hit additional
nodes to get row data but for just 1 client thread (to get simplest path to
show horizontal scaling, had a slight decrease of performance when going to
4 nodes from 2 nodes)
Things seen off of given procedure and set up:
1. 1 client thread: 2 nodes do better than 1 node on the query test.
But 4 nodes did not do better than 2.
2. 2 client threads: 2 nodes were still doing better than 1 node
3. 10 client threads: the times drastically suffered and 2 nodes were
doing 1/2 the speed of 1 node but before 1 to 2 threads performed better on
2 nodes vs 1 node. There was a huge decrease in performance on 2 nodes and
just a mild decrease on 1 node.
Note: 50+ threads was also drastically falling apart.
*Observations*:
- compacting each node to 1 table did not seem to help as running 10
client threads on exploded sstables and 2 nodes was 2x better than the last
2 node 10 client test but still decreased performance from 1 to 2 threads
query against compacted tables
- I would see upwards to 10 read requests pending at times while 8 to 10
were processing when I did nodetool tpstats.
- having key cache on or disabled did not seem to impact things
noticeably with our current configuration
.
*Questions:*
1. can multiple threads read the same sstable at the same time? Does
compacting down to 1 sstable (to get a given row into one sstable) add any
benefit or actually hurt like limited testing has indicated currently?
2. given the above testing process, does it still make sense to adjust
replication factor appropriately for cluster size (i.e. 1 for 1 node
cluster, 2 for 2 node cluster, 3 for n size cluster). We assumed it was
just the ability for threads to connect into a coordinator that would help
but sounds like it can still block
I'm going to try a limited test with changing replication factor. But if
anyone has any input on compacting to 1 sstable benefit or detriment on
just simple scalability test, how if at all does cassandra block on reading
sstables, and if higher replication factors do indeed help produce reliable
results it would be appreciated. I know part of our charter was keep it
simple to produce the scalability proof but it does sound like replication
factor is hurting us if the delay between clients for the same keys is not
long enough given the fact we are not doing different offsets for each
client thread.
Thanks,
Diane
On Thu, Jul 17, 2014 at 3:53 AM, Duncan Sands <duncan.sa...@gmail.com>
wrote:
> Hi Diane,
>
>
> On 17/07/14 06:19, Diane Griffith wrote:
>
>> We have been struggling proving out linear read performance with our
>> cassandra
>> configuration, that it is horizontally scaling. Wondering if anyone has
>> any
>> suggestions for what minimal configuration and approach to use to
>> demonstrate this.
>>
>> We were trying to go for a simple set up, so on the keyspace and/or column
>> families we went with the following settings thinking it was the minimal
>> to
>> prove scaling:
>>
>> replication_factor set to 1,
>>
>
> a RF of 1 means that any particular bit of data exists on exactly one
> node. So if you are testing read speed by reading the same data item again
> and again as fast as you can, then all the reads will be coming from the
> same one node, the one that has that data item on it. In this situation
> adding more nodes won't help. Maybe this isn't exactly how you are testing
> read speed, but perhaps you are doing something analogous? I suggest you
> explain how you are measuring read speed exactly.
>
> Ciao, Duncan.
>
> SimpleStrategy,
>> default consistency level,
>> default compaction strategy (size tiered),
>> but compacted down to 1 sstable per cf on each node (versus using leveled
>> compaction for read performance)
>>
>> *Read Performance Results:*
>>
>> 1 client thread - 2 nodes > 1 node was seen but we couldn't show increased
>> performance adding more nodes i.e 4 nodes ! > 2 nodes
>> 2 client threads - 2 nodes > 1 node still was true but again we couldn't
>> show
>> increased performance adding more nodes i.e. 4 nodes ! > 2 nodes
>> 10 client threads - this time 2 nodes < 1 node on performance numbers. 2
>> nodes
>> suffered from larger reduce throughput than 1 node was showing.
>>
>> Where are we going wrong?
>>
>> How have others shown horizontal scaling for reads?
>>
>> Thanks,
>> Diane
>>
>
>
