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From Alexander Dejanovski <a...@thelastpickle.com>
Subject Re: Too many tombstones using TTL
Date Tue, 16 Jan 2018 14:36:40 GMT
I would not plan on deleting data at the row level as you'll end up with a
lot of tombstones eventually (and you won't even notice them).
It's not healthy to allow that many tombstones to be read, and while your
latency may fit your SLA now, it may not in the future.
Tombstones are going to create a lot of heap pressure and eventually
trigger long GC pauses, which then tend to affect the whole cluster (a slow
node is worse than a down node).

You should definitely separate data that is TTLed and data that is not in
different tables so that you can adjust compaction strategies,
gc_grace_seconds and read patterns accordingly. I understand that it will
complexify your code, but it will prevent severe performance issues in
Cassandra.

Tombstones won't be a problem for repair, they will get repaired as classic
cells. They negatively affect the read path mostly, and use space on disk.

On Tue, Jan 16, 2018 at 2:12 PM Python_Max <python.max@gmail.com> wrote:

> Hello.
>
> I was planning to remove a row (not partition).
>
> Most of the tombstones are seen in the use case of geographic grid with
> X:Y as partition key and object id (timeuuid) as clustering key where
> objects could be temporary with TTL about 10 hours or fully persistent.
> When I select all objects in specific X:Y I can even hit 100k (default)
> limit for some X:Y. I have changed this limit to 500k since 99.9p read
> latency is < 75ms so I should not (?) care how many tombstones while read
> latency is fine.
>
> Splitting entities to temporary and permanent and using different
> compaction strategies is an option but it will lead to code duplication and
> 2x read queries.
>
> Is my assumption correct about tombstones are not so big problem as soon
> as read latency and disk usage are okey? Are tombstones affect repair time
> (using reaper)?
>
> Thanks.
>
>
> On Tue, Jan 16, 2018 at 11:32 AM, Alexander Dejanovski <
> alex@thelastpickle.com> wrote:
>
>> Hi,
>>
>> could you be more specific about the deletes you're planning to perform ?
>> This will end up moving your problem somewhere else as you'll be
>> generating new tombstones (and if you're planning on deleting rows, be
>> aware that row level tombstones aren't reported anywhere in the metrics,
>> logs and query traces).
>> Currently you can delete your data at the partition level, which will
>> create a single tombstone that will shadow all your expired (and non
>> expired) data and is very efficient. The read path is optimized for such
>> tombstones and the data won't be fully read from disk nor exchanged between
>> replicas. But that's of course if your use case allows to delete full
>> partitions.
>>
>> We usually model so that we can restrict our reads to live data.
>> If you're creating time series, your clustering key should include a
>> timestamp, which you can use to avoid reading expired data. If your TTL is
>> set to 60 days, you can read only data that is strictly younger than that.
>> Then you can partition by time ranges, and access exclusively partitions
>> that have no chance to be expired yet.
>> Those techniques usually work better with TWCS, but the former could make
>> you hit a lot of SSTables if your partitions can spread over all time
>> buckets, so only use TWCS if you can restrict individual reads to up to 4
>> time windows.
>>
>> Cheers,
>>
>>
>> On Tue, Jan 16, 2018 at 10:01 AM Python_Max <python.max@gmail.com> wrote:
>>
>>> Hi.
>>>
>>> Thank you very much for detailed explanation.
>>> Seems that there is nothing I can do about it except delete records by
>>> key instead of expiring.
>>>
>>>
>>> On Fri, Jan 12, 2018 at 7:30 PM, Alexander Dejanovski <
>>> alex@thelastpickle.com> wrote:
>>>
>>>> Hi,
>>>>
>>>> As DuyHai said, different TTLs could theoretically be set for different
>>>> cells of the same row. And one TTLed cell could be shadowing another cell
>>>> that has no TTL (say you forgot to set a TTL and set one afterwards by
>>>> performing an update), or vice versa.
>>>> One cell could also be missing from a node without Cassandra knowing.
>>>> So turning an incomplete row that only has expired cells into a tombstone
>>>> row could lead to wrong results being returned at read time : the tombstone
>>>> row could potentially shadow a valid live cell from another replica.
>>>>
>>>> Cassandra needs to retain each TTLed cell and send it to replicas
>>>> during reads to cover all possible cases.
>>>>
>>>>
>>>> On Fri, Jan 12, 2018 at 5:28 PM Python_Max <python.max@gmail.com>
>>>> wrote:
>>>>
>>>>> Thank you for response.
>>>>>
>>>>> I know about the option of setting TTL per column or even per item in
>>>>> collection. However in my example entire row has expired, shouldn't
>>>>> Cassandra be able to detect this situation and spawn a single tombstone
for
>>>>> entire row instead of many?
>>>>> Is there any reason not doing this except that no one needs it? Is
>>>>> this suitable for feature request or improvement?
>>>>>
>>>>> Thanks.
>>>>>
>>>>> On Wed, Jan 10, 2018 at 4:52 PM, DuyHai Doan <doanduyhai@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> "The question is why Cassandra creates a tombstone for every column
>>>>>> instead of single tombstone per row?"
>>>>>>
>>>>>> --> Simply because technically it is possible to set different
TTL
>>>>>> value on each column of a CQL row
>>>>>>
>>>>>> On Wed, Jan 10, 2018 at 2:59 PM, Python_Max <python.max@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hello, C* users and experts.
>>>>>>>
>>>>>>> I have (one more) question about tombstones.
>>>>>>>
>>>>>>> Consider the following example:
>>>>>>> cqlsh> create keyspace test_ttl with replication = {'class':
>>>>>>> 'SimpleStrategy', 'replication_factor': '1'}; use test_ttl;
>>>>>>> cqlsh> create table items(a text, b text, c1 text, c2 text,
c3 text,
>>>>>>> primary key (a, b));
>>>>>>> cqlsh> insert into items(a,b,c1,c2,c3) values('AAA', 'BBB',
'C111',
>>>>>>> 'C222', 'C333') using ttl 60;
>>>>>>> bash$ nodetool flush
>>>>>>> bash$ sleep 60
>>>>>>> bash$ nodetool compact test_ttl items
>>>>>>> bash$ sstabledump mc-2-big-Data.db
>>>>>>>
>>>>>>> [
>>>>>>>   {
>>>>>>>     "partition" : {
>>>>>>>       "key" : [ "AAA" ],
>>>>>>>       "position" : 0
>>>>>>>     },
>>>>>>>     "rows" : [
>>>>>>>       {
>>>>>>>         "type" : "row",
>>>>>>>         "position" : 58,
>>>>>>>         "clustering" : [ "BBB" ],
>>>>>>>         "liveness_info" : { "tstamp" : "2018-01-10T13:29:25.777Z",
>>>>>>> "ttl" : 60, "expires_at" : "2018-01-10T13:30:25Z", "expired"
: true },
>>>>>>>         "cells" : [
>>>>>>>           { "name" : "c1", "deletion_info" : { "local_delete_time"
:
>>>>>>> "2018-01-10T13:29:25Z" }
>>>>>>>           },
>>>>>>>           { "name" : "c2", "deletion_info" : { "local_delete_time"
:
>>>>>>> "2018-01-10T13:29:25Z" }
>>>>>>>           },
>>>>>>>           { "name" : "c3", "deletion_info" : { "local_delete_time"
:
>>>>>>> "2018-01-10T13:29:25Z" }
>>>>>>>           }
>>>>>>>         ]
>>>>>>>       }
>>>>>>>     ]
>>>>>>>   }
>>>>>>> ]
>>>>>>>
>>>>>>> The question is why Cassandra creates a tombstone for every column
>>>>>>> instead of single tombstone per row?
>>>>>>>
>>>>>>> In production environment I have a table with ~30 columns and
It
>>>>>>> gives me a warning for 30k tombstones and 300 live rows. It is
30 times
>>>>>>> more then it could be.
>>>>>>> Can this behavior be tuned in some way?
>>>>>>>
>>>>>>> Thanks.
>>>>>>>
>>>>>>> --
>>>>>>> Best regards,
>>>>>>> Python_Max.
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Best regards,
>>>>> Python_Max.
>>>>>
>>>>
>>>>
>>>> --
>>>> -----------------
>>>> Alexander Dejanovski
>>>> France
>>>> @alexanderdeja
>>>>
>>>> Consultant
>>>> Apache Cassandra Consulting
>>>> http://www.thelastpickle.com
>>>>
>>>
>>>
>>>
>>> --
>>> Best regards,
>>> Python_Max.
>>>
>>
>>
>> --
>> -----------------
>> Alexander Dejanovski
>> France
>> @alexanderdeja
>>
>> Consultant
>> Apache Cassandra Consulting
>> http://www.thelastpickle.com
>>
>
>
>
> --
> Best regards,
> Python_Max.
>


-- 
-----------------
Alexander Dejanovski
France
@alexanderdeja

Consultant
Apache Cassandra Consulting
http://www.thelastpickle.com

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