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From Ryan Svihla <rsvi...@datastax.com>
Subject Re: 100% CPU utilization, ParNew and never completing compactions
Date Wed, 17 Dec 2014 13:37:27 GMT
   1. Data loss could be a couple of possibilities. One you have to
   understand what is happening with a dropped mutation. This means that the
   know was never able to acknowledge that write even though it accepted a
   request to take it. Furthermore if you're writing at CL one and reading
   back at CL one..it's pretty trivial to get the 'wrong answer' with lots of
   dropped mutations, however, repairs will restore that data. The other
   possibility (if you're doing deletes) is that your servers are out of sync
   time wise, since the timestamp of a write will be determined by the
   coordinator node you can effectively have deletes in the future and so your
   writes never are stored. Tracing with CL ALL should give you more
   information on an individual case, whether there are tombstones in the
   path, or there is a different answer between servers. Final crappy answer
   is your client code is not actually catching exceptions correctly, I've
   seen people do this using async and  never blocking on the futures, so they
   never catch their exceptions.
   2. How much fragmentation are you seeing? what is your sstable count
   (histograms again)?
   3. Have you tried changing the heap settings to reduce the length of gc
   pauses.
   4. What is your disk setup? Are you using RAID 1? RAID 0? JBOD?
   5. Are you sure you're on the SSD drives of the instance? I know it
   sounds silly but I've had I can't tell you how many customers on EBS backed
   root drives, ignoring the much much faster local SSD drives that are
   provided with modern instances.
   6. Which compaction strategy are you on? What is the throughput?
   7. Have you upped flush writers?  the default setting is conservative,
   and if you're pushing through as fast of writes as you possibly can that
   maybe just too much for the default.
   8. Are you using LWT like IF NOT EXISTS on every insert?

It may pay to have someone with production Cassandra experience review
you're whole setup, from code to cassandra to ec2 tuning, this maybe a
multifaceted problem.

On Wed, Dec 17, 2014 at 2:22 AM, Arne Claassen <arne@emotient.com> wrote:
>
> Ok, tonight we rolled out on the production cluster. This one has 4 nodes
> and we dropped and recreated the keyspace before re-processing to avoid all
> possibility of  Everything seemed ok, even if the CPU load was pegged and
> we saw lots of MUTATION dropped message, but after all the reprocessing was
> done we noticed data loss, as in QA found some reports missing data.
> Looking at the app logs it showed 300 rows being written, but in C* there
> were only 4 rows.
>
> We brought up a brand new cluster, going to c3.2xlarge (more than doubling
> per node CPU) and increased the cluster to 6 nodes and turned processing
> down to do one media record at a time (still means a lot of rows written
> with a fanout of 50 async inserts at once). Even with that we're seeing
> fairly frequent MUTATION dropped messages.
>
> Clearly we're doing something fundamentally wrong but other than changing
> my inserts to batches, I just don't know what else i can do. We're pushing
> data loads that single server relational databases wouldn't be too
> concerned about right now.
>
> Any suggestions at all would be greatly appreciated.
>
> arne
>
> On Dec 16, 2014, at 4:48 PM, Ryan Svihla <rsvihla@datastax.com> wrote:
>
> What version of Cassandra?
> On Dec 16, 2014 6:36 PM, "Arne Claassen" <arne@emotient.com> wrote:
>
>> That's just the thing. There is nothing in the logs except the constant
>> ParNew collections like
>>
>> DEBUG [ScheduledTasks:1] 2014-12-16 19:03:35,042 GCInspector.java (line
>> 118) GC for ParNew: 166 ms for 10 collections, 4400928736 used; max is
>> 8000634888
>>
>> But the load is staying continuously high.
>>
>> There's always some compaction on just that one table, media_tracks_raw
>> going on and those values rarely changed (certainly the remaining time is
>> meaningless)
>>
>> pending tasks: 17
>>           compaction type        keyspace           table       completed
>>           total      unit  progress
>>                Compaction           mediamedia_tracks_raw       444294932
>>      1310653468     bytes    33.90%
>>                Compaction           mediamedia_tracks_raw       131931354
>>      3411631999     bytes     3.87%
>>                Compaction           mediamedia_tracks_raw        30308970
>>     23097672194     bytes     0.13%
>>                Compaction           mediamedia_tracks_raw       899216961
>>      1815591081     bytes    49.53%
>> Active compaction remaining time :   0h27m56s
>>
>> Here's a sample of a query trace:
>>
>>  activity
>>                         | timestamp    | source        | source_elapsed
>>
>> --------------------------------------------------------------------------------------------------+--------------+---------------+----------------
>>
>>      execute_cql3_query | 00:11:46,612 | 10.140.22.236 |              0
>>  Parsing select * from media_tracks_raw where id
>> =74fe9449-8ac4-accb-a723-4bad024101e3 limit 100; | 00:11:46,612 |
>> 10.140.22.236 |             47
>>
>>     Preparing statement | 00:11:46,612 | 10.140.22.236 |            234
>>                                                                  Sending
>> message to /10.140.21.54 | 00:11:46,619 | 10.140.22.236 |           7190
>>                                                              Message
>> received from /10.140.22.236 | 00:11:46,622 |  10.140.21.54 |
>>   12
>>                                              Executing single-partition
>> query on media_tracks_raw | 00:11:46,644 |  10.140.21.54 |          21971
>>
>>  Acquiring sstable references | 00:11:46,644 |  10.140.21.54 |
>>  22029
>>
>> Merging memtable tombstones | 00:11:46,644 |  10.140.21.54 |          22131
>>                                                         Bloom filter
>> allows skipping sstable 1395 | 00:11:46,644 |  10.140.21.54 |          22245
>>                                                         Bloom filter
>> allows skipping sstable 1394 | 00:11:46,644 |  10.140.21.54 |          22279
>>                                                         Bloom filter
>> allows skipping sstable 1391 | 00:11:46,644 |  10.140.21.54 |          22293
>>                                                         Bloom filter
>> allows skipping sstable 1381 | 00:11:46,644 |  10.140.21.54 |          22304
>>                                                         Bloom filter
>> allows skipping sstable 1376 | 00:11:46,644 |  10.140.21.54 |          22317
>>                                                         Bloom filter
>> allows skipping sstable 1368 | 00:11:46,644 |  10.140.21.54 |          22328
>>                                                         Bloom filter
>> allows skipping sstable 1365 | 00:11:46,644 |  10.140.21.54 |          22340
>>                                                         Bloom filter
>> allows skipping sstable 1351 | 00:11:46,644 |  10.140.21.54 |          22352
>>                                                         Bloom filter
>> allows skipping sstable 1367 | 00:11:46,644 |  10.140.21.54 |          22363
>>                                                         Bloom filter
>> allows skipping sstable 1380 | 00:11:46,644 |  10.140.21.54 |          22374
>>                                                         Bloom filter
>> allows skipping sstable 1343 | 00:11:46,644 |  10.140.21.54 |          22386
>>                                                         Bloom filter
>> allows skipping sstable 1342 | 00:11:46,644 |  10.140.21.54 |          22397
>>                                                         Bloom filter
>> allows skipping sstable 1334 | 00:11:46,644 |  10.140.21.54 |          22408
>>                                                         Bloom filter
>> allows skipping sstable 1377 | 00:11:46,644 |  10.140.21.54 |          22429
>>                                                         Bloom filter
>> allows skipping sstable 1330 | 00:11:46,644 |  10.140.21.54 |          22441
>>                                                         Bloom filter
>> allows skipping sstable 1329 | 00:11:46,644 |  10.140.21.54 |          22452
>>                                                         Bloom filter
>> allows skipping sstable 1328 | 00:11:46,644 |  10.140.21.54 |          22463
>>                                                         Bloom filter
>> allows skipping sstable 1327 | 00:11:46,644 |  10.140.21.54 |          22475
>>                                                         Bloom filter
>> allows skipping sstable 1326 | 00:11:46,644 |  10.140.21.54 |          22488
>>                                                         Bloom filter
>> allows skipping sstable 1320 | 00:11:46,644 |  10.140.21.54 |          22506
>>                                                         Bloom filter
>> allows skipping sstable 1319 | 00:11:46,644 |  10.140.21.54 |          22518
>>                                                         Bloom filter
>> allows skipping sstable 1318 | 00:11:46,644 |  10.140.21.54 |          22528
>>                                                         Bloom filter
>> allows skipping sstable 1317 | 00:11:46,644 |  10.140.21.54 |          22540
>>                                                         Bloom filter
>> allows skipping sstable 1316 | 00:11:46,644 |  10.140.21.54 |          22552
>>                                                         Bloom filter
>> allows skipping sstable 1315 | 00:11:46,644 |  10.140.21.54 |          22563
>>                                                         Bloom filter
>> allows skipping sstable 1314 | 00:11:46,644 |  10.140.21.54 |          22572
>>                                                         Bloom filter
>> allows skipping sstable 1313 | 00:11:46,644 |  10.140.21.54 |          22583
>>                                                         Bloom filter
>> allows skipping sstable 1312 | 00:11:46,644 |  10.140.21.54 |          22594
>>                                                         Bloom filter
>> allows skipping sstable 1311 | 00:11:46,644 |  10.140.21.54 |          22605
>>                                                         Bloom filter
>> allows skipping sstable 1310 | 00:11:46,644 |  10.140.21.54 |          22616
>>                                                         Bloom filter
>> allows skipping sstable 1309 | 00:11:46,644 |  10.140.21.54 |          22628
>>                                                         Bloom filter
>> allows skipping sstable 1308 | 00:11:46,644 |  10.140.21.54 |          22640
>>                                                         Bloom filter
>> allows skipping sstable 1307 | 00:11:46,644 |  10.140.21.54 |          22651
>>                                                         Bloom filter
>> allows skipping sstable 1306 | 00:11:46,644 |  10.140.21.54 |          22663
>>                                                         Bloom filter
>> allows skipping sstable 1305 | 00:11:46,644 |  10.140.21.54 |          22674
>>                                                         Bloom filter
>> allows skipping sstable 1304 | 00:11:46,644 |  10.140.21.54 |          22684
>>                                                         Bloom filter
>> allows skipping sstable 1303 | 00:11:46,644 |  10.140.21.54 |          22696
>>                                                         Bloom filter
>> allows skipping sstable 1302 | 00:11:46,644 |  10.140.21.54 |          22707
>>                                                         Bloom filter
>> allows skipping sstable 1301 | 00:11:46,644 |  10.140.21.54 |          22718
>>                                                         Bloom filter
>> allows skipping sstable 1300 | 00:11:46,644 |  10.140.21.54 |          22729
>>                                                         Bloom filter
>> allows skipping sstable 1299 | 00:11:46,644 |  10.140.21.54 |          22740
>>                                                         Bloom filter
>> allows skipping sstable 1298 | 00:11:46,644 |  10.140.21.54 |          22752
>>                                                         Bloom filter
>> allows skipping sstable 1297 | 00:11:46,644 |  10.140.21.54 |          22763
>>                                                         Bloom filter
>> allows skipping sstable 1296 | 00:11:46,644 |  10.140.21.54 |          22774
>>                                                                    Key
>> cache hit for sstable 1295 | 00:11:46,644 |  10.140.21.54 |          22817
>>                                                       Seeking to
>> partition beginning in data file | 00:11:46,644 |  10.140.21.54 |
>>  22842
>>                        Skipped 0/89 non-slice-intersecting sstables,
>> included 0 due to tombstones | 00:11:46,646 |  10.140.21.54 |          24109
>>                                                        Merging data from
>> memtables and 1 sstables | 00:11:46,646 |  10.140.21.54 |          24238
>>                                                              Read 101
>> live and 0 tombstoned cells | 00:11:46,663 |  10.140.21.54 |          41389
>>                                                              Enqueuing
>> response to /10.140.22.236 | 00:11:46,663 |  10.140.21.54 |
>>  41831
>>                                                                 Sending
>> message to /10.140.22.236 | 00:11:46,664 |  10.140.21.54 |          41972
>>                                                               Message
>> received from /10.140.21.54 | 00:11:46,671 | 10.140.22.236 |
>>  59498
>>                                                            Processing
>> response from /10.140.21.54 | 00:11:46,672 | 10.140.22.236 |
>>  59563
>>
>>        Request complete | 00:11:46,704 | 10.140.22.236 |          92781
>>
>> Every query I did always just had three mentions of tombstones
>>   Merging memtable tombstones
>>   Skipped 0/89 non-slice-intersecting sstables, included 0 due to
>> tombstones
>>   Read 101 live and 0 tombstoned cells
>> And unless i misread those, not of them claim that there are any
>> tombstones.
>>
>>
>> On Dec 16, 2014, at 4:26 PM, Ryan Svihla <rsvihla@datastax.com> wrote:
>>
>> manual forced compactions create more problems than they solve, if you
>> have no evidence of tombstones in your selects (which seems odd, can you
>> share some of the tracing output?), then I'm not sure what it would solve
>> for you.
>>
>> Compaction running could explain a high load, logs messages with ERRORS,
>> WARN, GCInspector are all meaningful there, I suggest search jira for your
>> version to see if there are any interesting bugs.
>>
>>
>>
>> On Tue, Dec 16, 2014 at 6:14 PM, Arne Claassen <arne@emotient.com> wrote:
>>>
>>> I just did a wide set of selects and ran across no tombstones. But while
>>> on the subject of gc_grace_seconds, any reason, on a small cluster not to
>>> set it to something low like a single day. It seems like 10 days is only
>>> need to large clusters undergoing long partition splits, or am i
>>> misunderstanding gc_grace_seconds.
>>>
>>> Now, given all that, does any of this explain a high load when the
>>> cluster is idle? Is it compaction catching up and would manual forced
>>> compaction alleviate that?
>>>
>>> thanks,
>>> arne
>>>
>>>
>>> On Dec 16, 2014, at 3:28 PM, Ryan Svihla <rsvihla@datastax.com> wrote:
>>>
>>> so a delete is really another write for gc_grace_seconds (default 10
>>> days), if you get enough tombstones it can make managing your cluster a
>>> challenge as is. open up cqlsh, turn on tracing and try a few queries..how
>>> many tombstones are scanned for a given query? It's possible the heap
>>> problems you're seeing are actually happening on the query side and not on
>>> the ingest side, the severity of this depends on driver and cassandra
>>> version, but older drivers and versions of cassandra could easily overload
>>> heap with expensive selects, when layered over tombstones it's certainly
>>> becomes a possibility this is your root cause.
>>>
>>> Now this will primarily create more load on compaction and depending on
>>> your cassandra version there maybe some other issue at work, but something
>>> I can tell you is every time I see 1 dropped mutation I see a cluster that
>>> was overloaded enough it had to shed load. If I see 200k I see a
>>> cluster/configuration/hardware that is badly overloaded.
>>>
>>> I suggest the following
>>>
>>>    - trace some of the queries used in prod
>>>    - monitor your ingest rate, see at what levels you run into issues
>>>    (GCInspector log messages, dropped mutations, etc)
>>>    - heap configuration we mentioned earlier..go ahead and monitor heap
>>>    usage, if it hits 75% repeated this is an indication of heavy load
>>>    - monitor dropped mutations..any dropped mutation is evidence of an
>>>    overloaded server, again the root cause can be many other problems that are
>>>    solvable with current hardware, and LOTS of people runs with nodes with
>>>    similar configuration.
>>>
>>>
>>> On Tue, Dec 16, 2014 at 5:08 PM, Arne Claassen <arne@emotient.com>
>>> wrote:
>>>>
>>>> Not using any secondary indicies and memtable_flush_queue_size is the
>>>> default 4.
>>>>
>>>> But let me tell you how data is "mutated" right now, maybe that will
>>>> give you an insight on how this is happening
>>>>
>>>> Basically the frame data table has the following primary key: PRIMARY
>>>> KEY ((id), trackid, "timestamp")
>>>>
>>>> Generally data is inserted once. So day to day writes are all new rows.
>>>> However, when out process for generating analytics for these rows
>>>> changes, we run the media back through again, causing overwrites.
>>>>
>>>> Up until last night, this was just a new insert because the PK never
>>>> changed so it was always 1-to-1 overwrite of every row.
>>>>
>>>> Last night was the first time that a new change went in where the PK
>>>> could actually change so now the process is always, DELETE by partition
>>>> key, insert all rows for partition key, repeat.
>>>>
>>>> We two tables that have similar frame data projections and some other
>>>> aggregates with much smaller row count per partition key.
>>>>
>>>> hope that helps,
>>>> arne
>>>>
>>>> On Dec 16, 2014, at 2:46 PM, Ryan Svihla <rsvihla@datastax.com> wrote:
>>>>
>>>> so you've got some blocked flush writers but you have a incredibly
>>>> large number of dropped mutations, are you using secondary indexes? and if
>>>> so how many? what is your flush queue set to?
>>>>
>>>> On Tue, Dec 16, 2014 at 4:43 PM, Arne Claassen <arne@emotient.com>
>>>> wrote:
>>>>>
>>>>> Of course QA decided to start a test batch (still relatively low
>>>>> traffic), so I hope it doesn't throw the tpstats off too much
>>>>>
>>>>> Node 1:
>>>>> Pool Name                    Active   Pending      Completed   Blocked
>>>>>  All time blocked
>>>>> MutationStage                     0         0       13804928         0
>>>>>                 0
>>>>> ReadStage                         0         0          10975         0
>>>>>                 0
>>>>> RequestResponseStage              0         0        7725378         0
>>>>>                 0
>>>>> ReadRepairStage                   0         0           1247         0
>>>>>                 0
>>>>> ReplicateOnWriteStage             0         0              0         0
>>>>>                 0
>>>>> MiscStage                         0         0              0         0
>>>>>                 0
>>>>> HintedHandoff                     1         1             50         0
>>>>>                 0
>>>>> FlushWriter                       0         0            306         0
>>>>>                31
>>>>> MemoryMeter                       0         0            719         0
>>>>>                 0
>>>>> GossipStage                       0         0         286505         0
>>>>>                 0
>>>>> CacheCleanupExecutor              0         0              0         0
>>>>>                 0
>>>>> InternalResponseStage             0         0              0         0
>>>>>                 0
>>>>> CompactionExecutor                4        14            159         0
>>>>>                 0
>>>>> ValidationExecutor                0         0              0         0
>>>>>                 0
>>>>> MigrationStage                    0         0              0         0
>>>>>                 0
>>>>> commitlog_archiver                0         0              0         0
>>>>>                 0
>>>>> AntiEntropyStage                  0         0              0         0
>>>>>                 0
>>>>> PendingRangeCalculator            0         0             11         0
>>>>>                 0
>>>>> MemtablePostFlusher               0         0           1781         0
>>>>>                 0
>>>>>
>>>>> Message type           Dropped
>>>>> READ                         0
>>>>> RANGE_SLICE                  0
>>>>> _TRACE                       0
>>>>> MUTATION                391041
>>>>> COUNTER_MUTATION             0
>>>>> BINARY                       0
>>>>> REQUEST_RESPONSE             0
>>>>> PAGED_RANGE                  0
>>>>> READ_REPAIR                  0
>>>>>
>>>>> Node 2:
>>>>> Pool Name                    Active   Pending      Completed   Blocked
>>>>>  All time blocked
>>>>> MutationStage                     0         0         997042         0
>>>>>                 0
>>>>> ReadStage                         0         0           2623         0
>>>>>                 0
>>>>> RequestResponseStage              0         0         706650         0
>>>>>                 0
>>>>> ReadRepairStage                   0         0            275         0
>>>>>                 0
>>>>> ReplicateOnWriteStage             0         0              0         0
>>>>>                 0
>>>>> MiscStage                         0         0              0         0
>>>>>                 0
>>>>> HintedHandoff                     2         2             12         0
>>>>>                 0
>>>>> FlushWriter                       0         0             37         0
>>>>>                 4
>>>>> MemoryMeter                       0         0             70         0
>>>>>                 0
>>>>> GossipStage                       0         0          14927         0
>>>>>                 0
>>>>> CacheCleanupExecutor              0         0              0         0
>>>>>                 0
>>>>> InternalResponseStage             0         0              0         0
>>>>>                 0
>>>>> CompactionExecutor                4         7             94         0
>>>>>                 0
>>>>> ValidationExecutor                0         0              0         0
>>>>>                 0
>>>>> MigrationStage                    0         0              0         0
>>>>>                 0
>>>>> commitlog_archiver                0         0              0         0
>>>>>                 0
>>>>> AntiEntropyStage                  0         0              0         0
>>>>>                 0
>>>>> PendingRangeCalculator            0         0              3         0
>>>>>                 0
>>>>> MemtablePostFlusher               0         0            114         0
>>>>>                 0
>>>>>
>>>>> Message type           Dropped
>>>>> READ                         0
>>>>> RANGE_SLICE                  0
>>>>> _TRACE                       0
>>>>> MUTATION                     0
>>>>> COUNTER_MUTATION             0
>>>>> BINARY                       0
>>>>> REQUEST_RESPONSE             0
>>>>> PAGED_RANGE                  0
>>>>> READ_REPAIR                  0
>>>>>
>>>>> Node 3:
>>>>> Pool Name                    Active   Pending      Completed   Blocked
>>>>>  All time blocked
>>>>> MutationStage                     0         0        1539324         0
>>>>>                 0
>>>>> ReadStage                         0         0           2571         0
>>>>>                 0
>>>>> RequestResponseStage              0         0         373300         0
>>>>>                 0
>>>>> ReadRepairStage                   0         0            325         0
>>>>>                 0
>>>>> ReplicateOnWriteStage             0         0              0         0
>>>>>                 0
>>>>> MiscStage                         0         0              0         0
>>>>>                 0
>>>>> HintedHandoff                     1         1             21         0
>>>>>                 0
>>>>> FlushWriter                       0         0             38         0
>>>>>                 5
>>>>> MemoryMeter                       0         0             59         0
>>>>>                 0
>>>>> GossipStage                       0         0          21491         0
>>>>>                 0
>>>>> CacheCleanupExecutor              0         0              0         0
>>>>>                 0
>>>>> InternalResponseStage             0         0              0         0
>>>>>                 0
>>>>> CompactionExecutor                4         9             85         0
>>>>>                 0
>>>>> ValidationExecutor                0         0              0         0
>>>>>                 0
>>>>> MigrationStage                    0         0              0         0
>>>>>                 0
>>>>> commitlog_archiver                0         0              0         0
>>>>>                 0
>>>>> AntiEntropyStage                  0         0              0         0
>>>>>                 0
>>>>> PendingRangeCalculator            0         0              6         0
>>>>>                 0
>>>>> MemtablePostFlusher               0         0            164         0
>>>>>                 0
>>>>>
>>>>> Message type           Dropped
>>>>> READ                         0
>>>>> RANGE_SLICE                  0
>>>>> _TRACE                       0
>>>>> MUTATION                205259
>>>>> COUNTER_MUTATION             0
>>>>> BINARY                       0
>>>>> REQUEST_RESPONSE             0
>>>>> PAGED_RANGE                  0
>>>>> READ_REPAIR                 18
>>>>>
>>>>>
>>>>> Compaction seems like the only thing consistently active and pending
>>>>>
>>>>> On Tue, Dec 16, 2014 at 2:18 PM, Ryan Svihla <rsvihla@datastax.com>
>>>>> wrote:
>>>>>>
>>>>>> Ok based on those numbers I have a theory..
>>>>>>
>>>>>> can you show me nodetool tptats for all 3 nodes?
>>>>>>
>>>>>> On Tue, Dec 16, 2014 at 4:04 PM, Arne Claassen <arne@emotient.com>
>>>>>> wrote:
>>>>>>>
>>>>>>> No problem with the follow up questions. I'm on a crash course here
>>>>>>> trying to understand what makes C* tick so I appreciate all feedback.
>>>>>>>
>>>>>>> We reprocessed all media (1200 partition keys) last night where
>>>>>>> partition keys had somewhere between 4k and 200k "rows". After that
>>>>>>> completed, no traffic went to cluster at all for ~8 hours and throughout
>>>>>>> today, we may get a couple (less than 10) queries per second and maybe 3-4
>>>>>>> write batches per hour.
>>>>>>>
>>>>>>> I assume the last value in the Partition Size histogram is the
>>>>>>> largest row:
>>>>>>>
>>>>>>> 20924300 bytes: 79
>>>>>>> 25109160 bytes: 57
>>>>>>>
>>>>>>> The majority seems clustered around 200000 bytes.
>>>>>>>
>>>>>>> I will look at switching my inserts to unlogged batches since they
>>>>>>> are always for one partition key.
>>>>>>>
>>>>>>> On Tue, Dec 16, 2014 at 1:47 PM, Ryan Svihla <rsvihla@datastax.com>
>>>>>>> wrote:
>>>>>>>>
>>>>>>>> Can you define what is "virtual no traffic" sorry to be repetitive
>>>>>>>> about that, but I've worked on a lot of clusters in the past year and
>>>>>>>> people have wildly different ideas what that means.
>>>>>>>>
>>>>>>>> unlogged batches of the same partition key are definitely a
>>>>>>>> performance optimization. Typically async is much faster and easier on the
>>>>>>>> cluster when you're using multip partition key batches.
>>>>>>>>
>>>>>>>> nodetool cfhistograms <keyspace> <tablename>
>>>>>>>>
>>>>>>>> On Tue, Dec 16, 2014 at 3:42 PM, Arne Claassen <arne@emotient.com>
>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>> Actually not sure why the machine was originally configured at 6GB
>>>>>>>>> since we even started it on an r3.large with 15GB.
>>>>>>>>>
>>>>>>>>> Re: Batches
>>>>>>>>>
>>>>>>>>> Not using batches. I actually have that as a separate question on
>>>>>>>>> the list. Currently I fan out async single inserts and I'm wondering if
>>>>>>>>> batches are better since my data is inherently inserted in blocks of
>>>>>>>>> ordered rows for a single partition key.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Re: Traffic
>>>>>>>>>
>>>>>>>>> There isn't all that much traffic. Inserts come in as blocks per
>>>>>>>>> partition key, but then can be 5k-200k rows for that partition key. Each of
>>>>>>>>> these rows is less than 100k. It's small, lots of ordered rows. It's frame
>>>>>>>>> and sub-frame information for media. and rows for one piece of media is
>>>>>>>>> inserted at once (the partition key).
>>>>>>>>>
>>>>>>>>> For the last 12 hours, where the load on all these machine has
>>>>>>>>> been stuck there's been virtually no traffic at all. This is the nodes
>>>>>>>>> basically sitting idle, except that they had  load of 4 each.
>>>>>>>>>
>>>>>>>>> BTW, how do you determine widest row or for that matter number of
>>>>>>>>> tombstones in a row?
>>>>>>>>>
>>>>>>>>> thanks,
>>>>>>>>> arne
>>>>>>>>>
>>>>>>>>> On Tue, Dec 16, 2014 at 1:24 PM, Ryan Svihla <rsvihla@datastax.com
>>>>>>>>> > wrote:
>>>>>>>>>>
>>>>>>>>>> So 1024 is still a good 2.5 times what I'm suggesting, 6GB is
>>>>>>>>>> hardly enough to run Cassandra well in, especially if you're going full
>>>>>>>>>> bore on loads. However, you maybe just flat out be CPU bound on your write
>>>>>>>>>> throughput, how many TPS and what size writes do you have? Also what is
>>>>>>>>>> your widest row?
>>>>>>>>>>
>>>>>>>>>> Final question what is compaction throughput at?
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Tue, Dec 16, 2014 at 3:20 PM, Arne Claassen <arne@emotient.com
>>>>>>>>>> > wrote:
>>>>>>>>>>>
>>>>>>>>>>> The starting configuration I had, which is still running on two
>>>>>>>>>>> of the nodes, was 6GB Heap, 1024MB parnew which is close to what you are
>>>>>>>>>>> suggesting and those have been pegged at load 4 for the over 12 hours with
>>>>>>>>>>> hardly and read or write traffic. I will set one to 8GB/400MB and see if
>>>>>>>>>>> its load changes.
>>>>>>>>>>>
>>>>>>>>>>> On Tue, Dec 16, 2014 at 1:12 PM, Ryan Svihla <
>>>>>>>>>>> rsvihla@datastax.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> So heap of that size without some tuning will create a number
>>>>>>>>>>>> of problems (high cpu usage one of them), I suggest either 8GB heap and
>>>>>>>>>>>> 400mb parnew (which I'd only set that low for that low cpu count) , or
>>>>>>>>>>>> attempt the tunings as indicated in
>>>>>>>>>>>> https://issues.apache.org/jira/browse/CASSANDRA-8150
>>>>>>>>>>>>
>>>>>>>>>>>> On Tue, Dec 16, 2014 at 3:06 PM, Arne Claassen <
>>>>>>>>>>>> arne@emotient.com> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> Changed the 15GB node to 25GB heap and the nice CPU is down to
>>>>>>>>>>>>> ~20% now. Checked my dev cluster to see if the ParNew log entries are just
>>>>>>>>>>>>> par for the course, but not seeing them there. However, both have the
>>>>>>>>>>>>> following every 30 seconds:
>>>>>>>>>>>>>
>>>>>>>>>>>>> DEBUG [BatchlogTasks:1] 2014-12-16 21:00:44,898
>>>>>>>>>>>>> BatchlogManager.java (line 165) Started replayAllFailedBatches
>>>>>>>>>>>>> DEBUG [MemtablePostFlusher:1] 2014-12-16 21:00:44,899
>>>>>>>>>>>>> ColumnFamilyStore.java (line 866) forceFlush requested but everything is
>>>>>>>>>>>>> clean in batchlog
>>>>>>>>>>>>> DEBUG [BatchlogTasks:1] 2014-12-16 21:00:44,899
>>>>>>>>>>>>> BatchlogManager.java (line 200) Finished replayAllFailedBatches
>>>>>>>>>>>>>
>>>>>>>>>>>>> Is that just routine scheduled house-keeping or a sign of
>>>>>>>>>>>>> something else?
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Tue, Dec 16, 2014 at 12:52 PM, Arne Claassen <
>>>>>>>>>>>>> arne@emotient.com> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Sorry, I meant 15GB heap on the one machine that has less
>>>>>>>>>>>>>> nice CPU% now. The others are 6GB
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Tue, Dec 16, 2014 at 12:50 PM, Arne Claassen <
>>>>>>>>>>>>>> arne@emotient.com> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> AWS r3.xlarge, 30GB, but only using a Heap of 10GB, new 2GB
>>>>>>>>>>>>>>> because we might go c3.2xlarge instead if CPU is more important than RAM
>>>>>>>>>>>>>>> Storage is optimized EBS SSD (but iostat shows no real IO
>>>>>>>>>>>>>>> going on)
>>>>>>>>>>>>>>> Each node only has about 10GB with ownership of 67%, 64.7% &
>>>>>>>>>>>>>>> 68.3%.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The node on which I set the Heap to 10GB from 6GB the
>>>>>>>>>>>>>>> utlilization has dropped to 46%nice now, but the ParNew log messages still
>>>>>>>>>>>>>>> continue at the same pace. I'm gonna up the HEAP to 20GB for a bit, see if
>>>>>>>>>>>>>>> that brings that nice CPU further down.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> No TombstoneOverflowingExceptions.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Tue, Dec 16, 2014 at 11:50 AM, Ryan Svihla <
>>>>>>>>>>>>>>> rsvihla@datastax.com> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> What's CPU, RAM, Storage layer, and data density per node?
>>>>>>>>>>>>>>>> Exact heap settings would be nice. In the logs look for
>>>>>>>>>>>>>>>> TombstoneOverflowingException
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Tue, Dec 16, 2014 at 1:36 PM, Arne Claassen <
>>>>>>>>>>>>>>>> arne@emotient.com> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I'm running 2.0.10.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The data is all time series data and as we change our
>>>>>>>>>>>>>>>>> pipeline, we've been periodically been reprocessing the data sources, which
>>>>>>>>>>>>>>>>> causes each time series to be overwritten, i.e. every row per partition key
>>>>>>>>>>>>>>>>> is deleted and re-written, so I assume i've been collecting a bunch of
>>>>>>>>>>>>>>>>> tombstones.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Also, the presence of the ever present and never
>>>>>>>>>>>>>>>>> completing compaction types, i assumed were an artifact of tombstoning, but
>>>>>>>>>>>>>>>>> i fully admit to conjecture based on about ~20 blog posts and stackoverflow
>>>>>>>>>>>>>>>>> questions i've surveyed.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I doubled the Heap on one node and it changed nothing
>>>>>>>>>>>>>>>>> regarding the load or the ParNew log statements. New Generation Usage is
>>>>>>>>>>>>>>>>> 50%, Eden itself is 56%.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Anything else i should look at and report, let me know.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Tue, Dec 16, 2014 at 11:14 AM, Jonathan Lacefield <
>>>>>>>>>>>>>>>>> jlacefield@datastax.com> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Hello,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>   What version of Cassandra are you running?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>   If it's 2.0, we recently experienced something similar
>>>>>>>>>>>>>>>>>> with 8447 [1], which 8485 [2] should hopefully resolve.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>   Please note that 8447 is not related to tombstones.
>>>>>>>>>>>>>>>>>> Tombstone processing can put a lot of pressure on the heap as well. Why do
>>>>>>>>>>>>>>>>>> you think you have a lot of tombstones in that one particular table?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>   [1]
>>>>>>>>>>>>>>>>>> https://issues.apache.org/jira/browse/CASSANDRA-8447
>>>>>>>>>>>>>>>>>>   [2]
>>>>>>>>>>>>>>>>>> https://issues.apache.org/jira/browse/CASSANDRA-8485
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Jonathan
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> [image: datastax_logo.png]
>>>>>>>>>>>>>>>>>> Jonathan Lacefield
>>>>>>>>>>>>>>>>>> Solution Architect | (404) 822 3487 |
>>>>>>>>>>>>>>>>>> jlacefield@datastax.com
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> [image: linkedin.png]
>>>>>>>>>>>>>>>>>> <http://www.linkedin.com/in/jlacefield/> [image:
>>>>>>>>>>>>>>>>>> facebook.png] <https://www.facebook.com/datastax> [image:
>>>>>>>>>>>>>>>>>> twitter.png] <https://twitter.com/datastax> [image:
>>>>>>>>>>>>>>>>>> g+.png] <https://plus.google.com/+Datastax/about>
>>>>>>>>>>>>>>>>>> <http://feeds.feedburner.com/datastax>
>>>>>>>>>>>>>>>>>> <https://github.com/datastax/>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Tue, Dec 16, 2014 at 2:04 PM, Arne Claassen <
>>>>>>>>>>>>>>>>>> arne@emotient.com> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> I have a three node cluster that has been sitting at a
>>>>>>>>>>>>>>>>>>> load of 4 (for each node), 100% CPI utilization (although 92% nice) for
>>>>>>>>>>>>>>>>>>> that last 12 hours, ever since some significant writes finished. I'm trying
>>>>>>>>>>>>>>>>>>> to determine what tuning I should be doing to get it out of this state. The
>>>>>>>>>>>>>>>>>>> debug log is just an endless series of:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> DEBUG [ScheduledTasks:1] 2014-12-16 19:03:35,042
>>>>>>>>>>>>>>>>>>> GCInspector.java (line 118) GC for ParNew: 166 ms for 10 collections,
>>>>>>>>>>>>>>>>>>> 4400928736 used; max is 8000634880
>>>>>>>>>>>>>>>>>>> DEBUG [ScheduledTasks:1] 2014-12-16 19:03:36,043
>>>>>>>>>>>>>>>>>>> GCInspector.java (line 118) GC for ParNew: 165 ms for 10 collections,
>>>>>>>>>>>>>>>>>>> 4440011176 used; max is 8000634880
>>>>>>>>>>>>>>>>>>> DEBUG [ScheduledTasks:1] 2014-12-16 19:03:37,043
>>>>>>>>>>>>>>>>>>> GCInspector.java (line 118) GC for ParNew: 135 ms for 8 collections,
>>>>>>>>>>>>>>>>>>> 4402220568 used; max is 8000634880
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> iostat shows virtually no I/O.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Compaction may enter into this, but i don't really know
>>>>>>>>>>>>>>>>>>> what to make of compaction stats since they never change:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> [root@cassandra-37919c3a ~]# nodetool compactionstats
>>>>>>>>>>>>>>>>>>> pending tasks: 10
>>>>>>>>>>>>>>>>>>>           compaction type        keyspace
>>>>>>>>>>>>>>>>>>> table       completed           total      unit  progress
>>>>>>>>>>>>>>>>>>>                Compaction
>>>>>>>>>>>>>>>>>>> mediamedia_tracks_raw       271651482       563615497     bytes    48.20%
>>>>>>>>>>>>>>>>>>>                Compaction
>>>>>>>>>>>>>>>>>>> mediamedia_tracks_raw        30308910     21676695677     bytes     0.14%
>>>>>>>>>>>>>>>>>>>                Compaction
>>>>>>>>>>>>>>>>>>> mediamedia_tracks_raw      1198384080      1815603161     bytes    66.00%
>>>>>>>>>>>>>>>>>>> Active compaction remaining time :   0h22m24s
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> 5 minutes later:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> [root@cassandra-37919c3a ~]# nodetool compactionstats
>>>>>>>>>>>>>>>>>>> pending tasks: 9
>>>>>>>>>>>>>>>>>>>           compaction type        keyspace
>>>>>>>>>>>>>>>>>>> table       completed           total      unit  progress
>>>>>>>>>>>>>>>>>>>                Compaction
>>>>>>>>>>>>>>>>>>> mediamedia_tracks_raw       271651482       563615497     bytes    48.20%
>>>>>>>>>>>>>>>>>>>                Compaction
>>>>>>>>>>>>>>>>>>> mediamedia_tracks_raw        30308910     21676695677     bytes     0.14%
>>>>>>>>>>>>>>>>>>>                Compaction
>>>>>>>>>>>>>>>>>>> mediamedia_tracks_raw      1198384080      1815603161     bytes    66.00%
>>>>>>>>>>>>>>>>>>> Active compaction remaining time :   0h22m24s
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Sure the pending tasks went down by one, but the rest is
>>>>>>>>>>>>>>>>>>> identical. media_tracks_raw likely has a bunch of tombstones (can't figure
>>>>>>>>>>>>>>>>>>> out how to get stats on that).
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Is this behavior something that indicates that i need
>>>>>>>>>>>>>>>>>>> more Heap, larger new generation? Should I be manually running compaction
>>>>>>>>>>>>>>>>>>> on tables with lots of tombstones?
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Any suggestions or places to educate myself better on
>>>>>>>>>>>>>>>>>>> performance tuning would be appreciated.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> arne
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> --
>>>>>>>>>>>>>>>> [image: datastax_logo.png] <http://www.datastax.com/>
>>>>>>>>>>>>>>>> Ryan Svihla
>>>>>>>>>>>>>>>> Solution Architect
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> [image: twitter.png] <https://twitter.com/foundev> [image:
>>>>>>>>>>>>>>>> linkedin.png]
>>>>>>>>>>>>>>>> <http://www.linkedin.com/pub/ryan-svihla/12/621/727/>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> DataStax is the fastest, most scalable distributed database
>>>>>>>>>>>>>>>> technology, delivering Apache Cassandra to the world’s most innovative
>>>>>>>>>>>>>>>> enterprises. Datastax is built to be agile, always-on, and predictably
>>>>>>>>>>>>>>>> scalable to any size. With more than 500 customers in 45 countries, DataStax
>>>>>>>>>>>>>>>> is the database technology and transactional backbone of choice for the
>>>>>>>>>>>>>>>> worlds most innovative companies such as Netflix, Adobe, Intuit, and eBay.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> --
>>>>>>>>>>>> [image: datastax_logo.png] <http://www.datastax.com/>
>>>>>>>>>>>> Ryan Svihla
>>>>>>>>>>>> Solution Architect
>>>>>>>>>>>>
>>>>>>>>>>>> [image: twitter.png] <https://twitter.com/foundev> [image:
>>>>>>>>>>>> linkedin.png]
>>>>>>>>>>>> <http://www.linkedin.com/pub/ryan-svihla/12/621/727/>
>>>>>>>>>>>>
>>>>>>>>>>>> DataStax is the fastest, most scalable distributed database
>>>>>>>>>>>> technology, delivering Apache Cassandra to the world’s most innovative
>>>>>>>>>>>> enterprises. Datastax is built to be agile, always-on, and predictably
>>>>>>>>>>>> scalable to any size. With more than 500 customers in 45 countries, DataStax
>>>>>>>>>>>> is the database technology and transactional backbone of choice for the
>>>>>>>>>>>> worlds most innovative companies such as Netflix, Adobe, Intuit, and eBay.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> --
>>>>>>>>>> [image: datastax_logo.png] <http://www.datastax.com/>
>>>>>>>>>> Ryan Svihla
>>>>>>>>>> Solution Architect
>>>>>>>>>>
>>>>>>>>>> [image: twitter.png] <https://twitter.com/foundev> [image:
>>>>>>>>>> linkedin.png]
>>>>>>>>>> <http://www.linkedin.com/pub/ryan-svihla/12/621/727/>
>>>>>>>>>>
>>>>>>>>>> DataStax is the fastest, most scalable distributed database
>>>>>>>>>> technology, delivering Apache Cassandra to the world’s most innovative
>>>>>>>>>> enterprises. Datastax is built to be agile, always-on, and predictably
>>>>>>>>>> scalable to any size. With more than 500 customers in 45 countries, DataStax
>>>>>>>>>> is the database technology and transactional backbone of choice for the
>>>>>>>>>> worlds most innovative companies such as Netflix, Adobe, Intuit, and eBay.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>
>>>>>>>> --
>>>>>>>> [image: datastax_logo.png] <http://www.datastax.com/>
>>>>>>>> Ryan Svihla
>>>>>>>> Solution Architect
>>>>>>>>
>>>>>>>> [image: twitter.png] <https://twitter.com/foundev> [image:
>>>>>>>> linkedin.png] <http://www.linkedin.com/pub/ryan-svihla/12/621/727/>
>>>>>>>>
>>>>>>>> DataStax is the fastest, most scalable distributed database
>>>>>>>> technology, delivering Apache Cassandra to the world’s most innovative
>>>>>>>> enterprises. Datastax is built to be agile, always-on, and predictably
>>>>>>>> scalable to any size. With more than 500 customers in 45 countries, DataStax
>>>>>>>> is the database technology and transactional backbone of choice for the
>>>>>>>> worlds most innovative companies such as Netflix, Adobe, Intuit, and eBay.
>>>>>>>>
>>>>>>>>
>>>>>>
>>>>>> --
>>>>>> [image: datastax_logo.png] <http://www.datastax.com/>
>>>>>> Ryan Svihla
>>>>>> Solution Architect
>>>>>>
>>>>>> [image: twitter.png] <https://twitter.com/foundev> [image:
>>>>>> linkedin.png] <http://www.linkedin.com/pub/ryan-svihla/12/621/727/>
>>>>>>
>>>>>> DataStax is the fastest, most scalable distributed database
>>>>>> technology, delivering Apache Cassandra to the world’s most innovative
>>>>>> enterprises. Datastax is built to be agile, always-on, and predictably
>>>>>> scalable to any size. With more than 500 customers in 45 countries, DataStax
>>>>>> is the database technology and transactional backbone of choice for the
>>>>>> worlds most innovative companies such as Netflix, Adobe, Intuit, and eBay.
>>>>>>
>>>>>>
>>>>
>>>> --
>>>> [image: datastax_logo.png] <http://www.datastax.com/>
>>>> Ryan Svihla
>>>> Solution Architect
>>>>
>>>> [image: twitter.png] <https://twitter.com/foundev> [image:
>>>> linkedin.png] <http://www.linkedin.com/pub/ryan-svihla/12/621/727/>
>>>>
>>>> DataStax is the fastest, most scalable distributed database technology,
>>>> delivering Apache Cassandra to the world’s most innovative enterprises.
>>>> Datastax is built to be agile, always-on, and predictably scalable to any
>>>> size. With more than 500 customers in 45 countries, DataStax is the
>>>> database technology and transactional backbone of choice for the worlds
>>>> most innovative companies such as Netflix, Adobe, Intuit, and eBay.
>>>>
>>>>
>>>>
>>>
>>> --
>>> [image: datastax_logo.png] <http://www.datastax.com/>
>>> Ryan Svihla
>>> Solution Architect
>>>
>>> [image: twitter.png] <https://twitter.com/foundev> [image: linkedin.png]
>>> <http://www.linkedin.com/pub/ryan-svihla/12/621/727/>
>>>
>>> DataStax is the fastest, most scalable distributed database technology,
>>> delivering Apache Cassandra to the world’s most innovative enterprises.
>>> Datastax is built to be agile, always-on, and predictably scalable to any
>>> size. With more than 500 customers in 45 countries, DataStax is the
>>> database technology and transactional backbone of choice for the worlds
>>> most innovative companies such as Netflix, Adobe, Intuit, and eBay.
>>>
>>>
>>>
>>
>> --
>> [image: datastax_logo.png] <http://www.datastax.com/>
>> Ryan Svihla
>> Solution Architect
>>
>> [image: twitter.png] <https://twitter.com/foundev> [image: linkedin.png]
>> <http://www.linkedin.com/pub/ryan-svihla/12/621/727/>
>>
>> DataStax is the fastest, most scalable distributed database technology,
>> delivering Apache Cassandra to the world’s most innovative enterprises.
>> Datastax is built to be agile, always-on, and predictably scalable to any
>> size. With more than 500 customers in 45 countries, DataStax is the
>> database technology and transactional backbone of choice for the worlds
>> most innovative companies such as Netflix, Adobe, Intuit, and eBay.
>>
>>
>>
>

-- 

[image: datastax_logo.png] <http://www.datastax.com/>

Ryan Svihla

Solution Architect

[image: twitter.png] <https://twitter.com/foundev> [image: linkedin.png]
<http://www.linkedin.com/pub/ryan-svihla/12/621/727/>

DataStax is the fastest, most scalable distributed database technology,
delivering Apache Cassandra to the world’s most innovative enterprises.
Datastax is built to be agile, always-on, and predictably scalable to any
size. With more than 500 customers in 45 countries, DataStax is the
database technology and transactional backbone of choice for the worlds
most innovative companies such as Netflix, Adobe, Intuit, and eBay.

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