Re: Time series data model and tombstones

[DuyHai Doan]

Thanks for the update. Good to know that TWCS give you more stability

On Wed, Feb 8, 2017 at 6:20 PM, John Sanda  wrote:

> I wanted to provide a quick update. I was able to patch one of the
> environments that is hitting the tombstone problem. It has been running
> TWCS for five days now, and things are stable so far. I also had a patch to
> the application code to implement date partitioning ready to go, but I
> wanted to see how things went with only making the compaction changes.
>
> On Sun, Jan 29, 2017 at 4:05 PM, DuyHai Doan  wrote:
>
>> In theory, you're right and Cassandra should possibly skip reading cells
>> having time < 50. But it's all theory, in practice Cassandra read chunks of
>> xxx kilobytes worth of data (don't remember the exact value of xxx, maybe
>> 64k or far less) so you may end up reading tombstones.
>>
>> On Sun, Jan 29, 2017 at 9:24 PM, John Sanda  wrote:
>>
>>> Thanks for the clarification. Let's say I have a partition in an SSTable
>>> where the values of time range from 100 to 10 and everything < 50 is
>>> expired. If I do a query with time < 100 and time >= 50, are there
>>> scenarios in which Cassandra will have to read cells where time < 50? In
>>> particular I am wondering if compression might have any affect.
>>>
>>> On Sun, Jan 29, 2017 at 3:01 PM DuyHai Doan 
>>> wrote:
>>>
 "Should the data be sorted by my time column regardless of the
 compaction strategy" --> It does

 What I mean is that an old "chunk" of expired data in SSTABLE-12 may be
 compacted together with a new chunk of SSTABLE-2 containing fresh data so
 in the new resulting SSTable will contain tombstones AND fresh data inside
 the same partition, but of course sorted by clustering column "time".

 On Sun, Jan 29, 2017 at 8:55 PM, John Sanda 
 wrote:

 Since STCS does not sort data based on timestamp, your wide partition
 may span over multiple SSTables and inside each SSTable, old data (+
 tombstones) may sit on the same partition as newer data.


 Should the data be sorted by my time column regardless of the
 compaction strategy? I didn't think that the column timestamp came into
 play with respect to sorting. I have been able to review some SSTables with
 sstablemetadata and I can see that old/expired data is definitely living
 with live data.


 On Sun, Jan 29, 2017 at 2:38 PM, DuyHai Doan 
 wrote:

 Ok so give it a try with TWCS. Since STCS does not sort data based on
 timestamp, your wide partition may span over multiple SSTables and inside
 each SSTable, old data (+ tombstones) may sit on the same partition as
 newer data.

 When reading by slice, even if you request for fresh data, Cassandra
 has to scan over a lot tombstones to fetch the correct range of data thus
 your issue

 On Sun, Jan 29, 2017 at 8:19 PM, John Sanda 
 wrote:

 It was with STCS. It was on a 2.x version before TWCS was available.

 On Sun, Jan 29, 2017 at 10:58 AM DuyHai Doan 
 wrote:

 Did you get this Overwhelming tombstonne behavior with STCS or with
 TWCS ?

 If you're using DTCS, beware of its weird behavior and tricky
 configuration.

 On Sun, Jan 29, 2017 at 3:52 PM, John Sanda 
 wrote:

 Your partitioning key is text. If you have multiple entries per id you
 are likely hitting older cells that have expired. Descending only affects
 how the data is stored on disk, if you have to read the whole partition to
 find whichever time you are querying for you could potentially hit
 tombstones in other SSTables that contain the same "id". As mentioned
 previously, you need to add a time bucket to your partitioning key and
 definitely use DTCS/TWCS.


 As I mentioned previously, the UI only queries recent data, e.g., the
 past hour, past two hours, past day, past week. The UI does not query for
 anything older than the TTL which is 7 days. My understanding and
 expectation was that Cassandra would only scan live cells. The UI is a
 separate application that I do not maintain, so I am not 100% certain about
 the queries. I have been told that it does not query for anything older
 than 7 days.

 On Sun, Jan 29, 2017 at 4:14 AM, kurt greaves 
 wrote:


 Your partitioning key is text. If you have multiple entries per id you
 are likely hitting older cells that have expired. Descending only affects
 how the data is stored on disk, if you have to read the whole partition to
 find whichever time you are querying for you could potentially hit
 tombstones in other SSTables that contain the same "id". As mentioned

Re: Time series data model and tombstones

[John Sanda]

I wanted to provide a quick update. I was able to patch one of the
environments that is hitting the tombstone problem. It has been running
TWCS for five days now, and things are stable so far. I also had a patch to
the application code to implement date partitioning ready to go, but I
wanted to see how things went with only making the compaction changes.

On Sun, Jan 29, 2017 at 4:05 PM, DuyHai Doan  wrote:

> In theory, you're right and Cassandra should possibly skip reading cells
> having time < 50. But it's all theory, in practice Cassandra read chunks of
> xxx kilobytes worth of data (don't remember the exact value of xxx, maybe
> 64k or far less) so you may end up reading tombstones.
>
> On Sun, Jan 29, 2017 at 9:24 PM, John Sanda  wrote:
>
>> Thanks for the clarification. Let's say I have a partition in an SSTable
>> where the values of time range from 100 to 10 and everything < 50 is
>> expired. If I do a query with time < 100 and time >= 50, are there
>> scenarios in which Cassandra will have to read cells where time < 50? In
>> particular I am wondering if compression might have any affect.
>>
>> On Sun, Jan 29, 2017 at 3:01 PM DuyHai Doan  wrote:
>>
>>> "Should the data be sorted by my time column regardless of the
>>> compaction strategy" --> It does
>>>
>>> What I mean is that an old "chunk" of expired data in SSTABLE-12 may be
>>> compacted together with a new chunk of SSTABLE-2 containing fresh data so
>>> in the new resulting SSTable will contain tombstones AND fresh data inside
>>> the same partition, but of course sorted by clustering column "time".
>>>
>>> On Sun, Jan 29, 2017 at 8:55 PM, John Sanda 
>>> wrote:
>>>
>>> Since STCS does not sort data based on timestamp, your wide partition
>>> may span over multiple SSTables and inside each SSTable, old data (+
>>> tombstones) may sit on the same partition as newer data.
>>>
>>>
>>> Should the data be sorted by my time column regardless of the compaction
>>> strategy? I didn't think that the column timestamp came into play with
>>> respect to sorting. I have been able to review some SSTables with
>>> sstablemetadata and I can see that old/expired data is definitely living
>>> with live data.
>>>
>>>
>>> On Sun, Jan 29, 2017 at 2:38 PM, DuyHai Doan 
>>> wrote:
>>>
>>> Ok so give it a try with TWCS. Since STCS does not sort data based on
>>> timestamp, your wide partition may span over multiple SSTables and inside
>>> each SSTable, old data (+ tombstones) may sit on the same partition as
>>> newer data.
>>>
>>> When reading by slice, even if you request for fresh data, Cassandra has
>>> to scan over a lot tombstones to fetch the correct range of data thus your
>>> issue
>>>
>>> On Sun, Jan 29, 2017 at 8:19 PM, John Sanda 
>>> wrote:
>>>
>>> It was with STCS. It was on a 2.x version before TWCS was available.
>>>
>>> On Sun, Jan 29, 2017 at 10:58 AM DuyHai Doan 
>>> wrote:
>>>
>>> Did you get this Overwhelming tombstonne behavior with STCS or with TWCS
>>> ?
>>>
>>> If you're using DTCS, beware of its weird behavior and tricky
>>> configuration.
>>>
>>> On Sun, Jan 29, 2017 at 3:52 PM, John Sanda 
>>> wrote:
>>>
>>> Your partitioning key is text. If you have multiple entries per id you
>>> are likely hitting older cells that have expired. Descending only affects
>>> how the data is stored on disk, if you have to read the whole partition to
>>> find whichever time you are querying for you could potentially hit
>>> tombstones in other SSTables that contain the same "id". As mentioned
>>> previously, you need to add a time bucket to your partitioning key and
>>> definitely use DTCS/TWCS.
>>>
>>>
>>> As I mentioned previously, the UI only queries recent data, e.g., the
>>> past hour, past two hours, past day, past week. The UI does not query for
>>> anything older than the TTL which is 7 days. My understanding and
>>> expectation was that Cassandra would only scan live cells. The UI is a
>>> separate application that I do not maintain, so I am not 100% certain about
>>> the queries. I have been told that it does not query for anything older
>>> than 7 days.
>>>
>>> On Sun, Jan 29, 2017 at 4:14 AM, kurt greaves 
>>> wrote:
>>>
>>>
>>> Your partitioning key is text. If you have multiple entries per id you
>>> are likely hitting older cells that have expired. Descending only affects
>>> how the data is stored on disk, if you have to read the whole partition to
>>> find whichever time you are querying for you could potentially hit
>>> tombstones in other SSTables that contain the same "id". As mentioned
>>> previously, you need to add a time bucket to your partitioning key and
>>> definitely use DTCS/TWCS.
>>>
>>>
>>>
>>>
>>>
>>> --
>>>
>>> - John
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> --
>>>
>>> - John
>>>
>>>
>>>
>>>
>>>
>>>
>>>

Re: Time series data model and tombstones

[DuyHai Doan]

In theory, you're right and Cassandra should possibly skip reading cells
having time < 50. But it's all theory, in practice Cassandra read chunks of
xxx kilobytes worth of data (don't remember the exact value of xxx, maybe
64k or far less) so you may end up reading tombstones.

On Sun, Jan 29, 2017 at 9:24 PM, John Sanda  wrote:

> Thanks for the clarification. Let's say I have a partition in an SSTable
> where the values of time range from 100 to 10 and everything < 50 is
> expired. If I do a query with time < 100 and time >= 50, are there
> scenarios in which Cassandra will have to read cells where time < 50? In
> particular I am wondering if compression might have any affect.
>
> On Sun, Jan 29, 2017 at 3:01 PM DuyHai Doan  wrote:
>
>> "Should the data be sorted by my time column regardless of the
>> compaction strategy" --> It does
>>
>> What I mean is that an old "chunk" of expired data in SSTABLE-12 may be
>> compacted together with a new chunk of SSTABLE-2 containing fresh data so
>> in the new resulting SSTable will contain tombstones AND fresh data inside
>> the same partition, but of course sorted by clustering column "time".
>>
>> On Sun, Jan 29, 2017 at 8:55 PM, John Sanda  wrote:
>>
>> Since STCS does not sort data based on timestamp, your wide partition may
>> span over multiple SSTables and inside each SSTable, old data (+
>> tombstones) may sit on the same partition as newer data.
>>
>>
>> Should the data be sorted by my time column regardless of the compaction
>> strategy? I didn't think that the column timestamp came into play with
>> respect to sorting. I have been able to review some SSTables with
>> sstablemetadata and I can see that old/expired data is definitely living
>> with live data.
>>
>>
>> On Sun, Jan 29, 2017 at 2:38 PM, DuyHai Doan 
>> wrote:
>>
>> Ok so give it a try with TWCS. Since STCS does not sort data based on
>> timestamp, your wide partition may span over multiple SSTables and inside
>> each SSTable, old data (+ tombstones) may sit on the same partition as
>> newer data.
>>
>> When reading by slice, even if you request for fresh data, Cassandra has
>> to scan over a lot tombstones to fetch the correct range of data thus your
>> issue
>>
>> On Sun, Jan 29, 2017 at 8:19 PM, John Sanda  wrote:
>>
>> It was with STCS. It was on a 2.x version before TWCS was available.
>>
>> On Sun, Jan 29, 2017 at 10:58 AM DuyHai Doan 
>> wrote:
>>
>> Did you get this Overwhelming tombstonne behavior with STCS or with TWCS ?
>>
>> If you're using DTCS, beware of its weird behavior and tricky
>> configuration.
>>
>> On Sun, Jan 29, 2017 at 3:52 PM, John Sanda  wrote:
>>
>> Your partitioning key is text. If you have multiple entries per id you
>> are likely hitting older cells that have expired. Descending only affects
>> how the data is stored on disk, if you have to read the whole partition to
>> find whichever time you are querying for you could potentially hit
>> tombstones in other SSTables that contain the same "id". As mentioned
>> previously, you need to add a time bucket to your partitioning key and
>> definitely use DTCS/TWCS.
>>
>>
>> As I mentioned previously, the UI only queries recent data, e.g., the
>> past hour, past two hours, past day, past week. The UI does not query for
>> anything older than the TTL which is 7 days. My understanding and
>> expectation was that Cassandra would only scan live cells. The UI is a
>> separate application that I do not maintain, so I am not 100% certain about
>> the queries. I have been told that it does not query for anything older
>> than 7 days.
>>
>> On Sun, Jan 29, 2017 at 4:14 AM, kurt greaves 
>> wrote:
>>
>>
>> Your partitioning key is text. If you have multiple entries per id you
>> are likely hitting older cells that have expired. Descending only affects
>> how the data is stored on disk, if you have to read the whole partition to
>> find whichever time you are querying for you could potentially hit
>> tombstones in other SSTables that contain the same "id". As mentioned
>> previously, you need to add a time bucket to your partitioning key and
>> definitely use DTCS/TWCS.
>>
>>
>>
>>
>>
>> --
>>
>> - John
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> --
>>
>> - John
>>
>>
>>
>>
>>
>>
>>
>>

Re: Time series data model and tombstones

[Jonathan Haddad]

Check out our post on how to use TWCS before 3.0.

http://thelastpickle.com/blog/2017/01/10/twcs-part2.html

On Sun, Jan 29, 2017 at 11:20 AM John Sanda  wrote:

> It was with STCS. It was on a 2.x version before TWCS was available.
>
> On Sun, Jan 29, 2017 at 10:58 AM DuyHai Doan  wrote:
>
> Did you get this Overwhelming tombstonne behavior with STCS or with TWCS ?
>
> If you're using DTCS, beware of its weird behavior and tricky
> configuration.
>
> On Sun, Jan 29, 2017 at 3:52 PM, John Sanda  wrote:
>
> Your partitioning key is text. If you have multiple entries per id you are
> likely hitting older cells that have expired. Descending only affects how
> the data is stored on disk, if you have to read the whole partition to find
> whichever time you are querying for you could potentially hit tombstones in
> other SSTables that contain the same "id". As mentioned previously, you
> need to add a time bucket to your partitioning key and definitely use
> DTCS/TWCS.
>
>
> As I mentioned previously, the UI only queries recent data, e.g., the past
> hour, past two hours, past day, past week. The UI does not query for
> anything older than the TTL which is 7 days. My understanding and
> expectation was that Cassandra would only scan live cells. The UI is a
> separate application that I do not maintain, so I am not 100% certain about
> the queries. I have been told that it does not query for anything older
> than 7 days.
>
> On Sun, Jan 29, 2017 at 4:14 AM, kurt greaves 
> wrote:
>
>
> Your partitioning key is text. If you have multiple entries per id you are
> likely hitting older cells that have expired. Descending only affects how
> the data is stored on disk, if you have to read the whole partition to find
> whichever time you are querying for you could potentially hit tombstones in
> other SSTables that contain the same "id". As mentioned previously, you
> need to add a time bucket to your partitioning key and definitely use
> DTCS/TWCS.
>
>
>
>
>
> --
>
> - John
>
>
>
>
>
>
>
>

Re: Time series data model and tombstones

[John Sanda]

Thanks for the clarification. Let's say I have a partition in an SSTable
where the values of time range from 100 to 10 and everything < 50 is
expired. If I do a query with time < 100 and time >= 50, are there
scenarios in which Cassandra will have to read cells where time < 50? In
particular I am wondering if compression might have any affect.

On Sun, Jan 29, 2017 at 3:01 PM DuyHai Doan  wrote:

> "Should the data be sorted by my time column regardless of the compaction
> strategy" --> It does
>
> What I mean is that an old "chunk" of expired data in SSTABLE-12 may be
> compacted together with a new chunk of SSTABLE-2 containing fresh data so
> in the new resulting SSTable will contain tombstones AND fresh data inside
> the same partition, but of course sorted by clustering column "time".
>
> On Sun, Jan 29, 2017 at 8:55 PM, John Sanda  wrote:
>
> Since STCS does not sort data based on timestamp, your wide partition may
> span over multiple SSTables and inside each SSTable, old data (+
> tombstones) may sit on the same partition as newer data.
>
>
> Should the data be sorted by my time column regardless of the compaction
> strategy? I didn't think that the column timestamp came into play with
> respect to sorting. I have been able to review some SSTables with
> sstablemetadata and I can see that old/expired data is definitely living
> with live data.
>
>
> On Sun, Jan 29, 2017 at 2:38 PM, DuyHai Doan  wrote:
>
> Ok so give it a try with TWCS. Since STCS does not sort data based on
> timestamp, your wide partition may span over multiple SSTables and inside
> each SSTable, old data (+ tombstones) may sit on the same partition as
> newer data.
>
> When reading by slice, even if you request for fresh data, Cassandra has
> to scan over a lot tombstones to fetch the correct range of data thus your
> issue
>
> On Sun, Jan 29, 2017 at 8:19 PM, John Sanda  wrote:
>
> It was with STCS. It was on a 2.x version before TWCS was available.
>
> On Sun, Jan 29, 2017 at 10:58 AM DuyHai Doan  wrote:
>
> Did you get this Overwhelming tombstonne behavior with STCS or with TWCS ?
>
> If you're using DTCS, beware of its weird behavior and tricky
> configuration.
>
> On Sun, Jan 29, 2017 at 3:52 PM, John Sanda  wrote:
>
> Your partitioning key is text. If you have multiple entries per id you are
> likely hitting older cells that have expired. Descending only affects how
> the data is stored on disk, if you have to read the whole partition to find
> whichever time you are querying for you could potentially hit tombstones in
> other SSTables that contain the same "id". As mentioned previously, you
> need to add a time bucket to your partitioning key and definitely use
> DTCS/TWCS.
>
>
> As I mentioned previously, the UI only queries recent data, e.g., the past
> hour, past two hours, past day, past week. The UI does not query for
> anything older than the TTL which is 7 days. My understanding and
> expectation was that Cassandra would only scan live cells. The UI is a
> separate application that I do not maintain, so I am not 100% certain about
> the queries. I have been told that it does not query for anything older
> than 7 days.
>
> On Sun, Jan 29, 2017 at 4:14 AM, kurt greaves 
> wrote:
>
>
> Your partitioning key is text. If you have multiple entries per id you are
> likely hitting older cells that have expired. Descending only affects how
> the data is stored on disk, if you have to read the whole partition to find
> whichever time you are querying for you could potentially hit tombstones in
> other SSTables that contain the same "id". As mentioned previously, you
> need to add a time bucket to your partitioning key and definitely use
> DTCS/TWCS.
>
>
>
>
>
> --
>
> - John
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> --
>
> - John
>
>
>
>
>
>
>
>

Re: Time series data model and tombstones

[DuyHai Doan]

"Should the data be sorted by my time column regardless of the compaction
strategy" --> It does

What I mean is that an old "chunk" of expired data in SSTABLE-12 may be
compacted together with a new chunk of SSTABLE-2 containing fresh data so
in the new resulting SSTable will contain tombstones AND fresh data inside
the same partition, but of course sorted by clustering column "time".

On Sun, Jan 29, 2017 at 8:55 PM, John Sanda  wrote:

> Since STCS does not sort data based on timestamp, your wide partition may
>> span over multiple SSTables and inside each SSTable, old data (+
>> tombstones) may sit on the same partition as newer data.
>
>
> Should the data be sorted by my time column regardless of the compaction
> strategy? I didn't think that the column timestamp came into play with
> respect to sorting. I have been able to review some SSTables with
> sstablemetadata and I can see that old/expired data is definitely living
> with live data.
>
>
> On Sun, Jan 29, 2017 at 2:38 PM, DuyHai Doan  wrote:
>
>> Ok so give it a try with TWCS. Since STCS does not sort data based on
>> timestamp, your wide partition may span over multiple SSTables and inside
>> each SSTable, old data (+ tombstones) may sit on the same partition as
>> newer data.
>>
>> When reading by slice, even if you request for fresh data, Cassandra has
>> to scan over a lot tombstones to fetch the correct range of data thus your
>> issue
>>
>> On Sun, Jan 29, 2017 at 8:19 PM, John Sanda  wrote:
>>
>>> It was with STCS. It was on a 2.x version before TWCS was available.
>>>
>>> On Sun, Jan 29, 2017 at 10:58 AM DuyHai Doan 
>>> wrote:
>>>
 Did you get this Overwhelming tombstonne behavior with STCS or with
 TWCS ?

 If you're using DTCS, beware of its weird behavior and tricky
 configuration.

 On Sun, Jan 29, 2017 at 3:52 PM, John Sanda 
 wrote:

 Your partitioning key is text. If you have multiple entries per id you
 are likely hitting older cells that have expired. Descending only affects
 how the data is stored on disk, if you have to read the whole partition to
 find whichever time you are querying for you could potentially hit
 tombstones in other SSTables that contain the same "id". As mentioned
 previously, you need to add a time bucket to your partitioning key and
 definitely use DTCS/TWCS.


 As I mentioned previously, the UI only queries recent data, e.g., the
 past hour, past two hours, past day, past week. The UI does not query for
 anything older than the TTL which is 7 days. My understanding and
 expectation was that Cassandra would only scan live cells. The UI is a
 separate application that I do not maintain, so I am not 100% certain about
 the queries. I have been told that it does not query for anything older
 than 7 days.

 On Sun, Jan 29, 2017 at 4:14 AM, kurt greaves 
 wrote:


 Your partitioning key is text. If you have multiple entries per id you
 are likely hitting older cells that have expired. Descending only affects
 how the data is stored on disk, if you have to read the whole partition to
 find whichever time you are querying for you could potentially hit
 tombstones in other SSTables that contain the same "id". As mentioned
 previously, you need to add a time bucket to your partitioning key and
 definitely use DTCS/TWCS.





 --

 - John








>>
>
>
> --
>
> - John
>

Re: Time series data model and tombstones

[John Sanda]

>
> Since STCS does not sort data based on timestamp, your wide partition may
> span over multiple SSTables and inside each SSTable, old data (+
> tombstones) may sit on the same partition as newer data.


Should the data be sorted by my time column regardless of the compaction
strategy? I didn't think that the column timestamp came into play with
respect to sorting. I have been able to review some SSTables with
sstablemetadata and I can see that old/expired data is definitely living
with live data.


On Sun, Jan 29, 2017 at 2:38 PM, DuyHai Doan  wrote:

> Ok so give it a try with TWCS. Since STCS does not sort data based on
> timestamp, your wide partition may span over multiple SSTables and inside
> each SSTable, old data (+ tombstones) may sit on the same partition as
> newer data.
>
> When reading by slice, even if you request for fresh data, Cassandra has
> to scan over a lot tombstones to fetch the correct range of data thus your
> issue
>
> On Sun, Jan 29, 2017 at 8:19 PM, John Sanda  wrote:
>
>> It was with STCS. It was on a 2.x version before TWCS was available.
>>
>> On Sun, Jan 29, 2017 at 10:58 AM DuyHai Doan 
>> wrote:
>>
>>> Did you get this Overwhelming tombstonne behavior with STCS or with TWCS
>>> ?
>>>
>>> If you're using DTCS, beware of its weird behavior and tricky
>>> configuration.
>>>
>>> On Sun, Jan 29, 2017 at 3:52 PM, John Sanda 
>>> wrote:
>>>
>>> Your partitioning key is text. If you have multiple entries per id you
>>> are likely hitting older cells that have expired. Descending only affects
>>> how the data is stored on disk, if you have to read the whole partition to
>>> find whichever time you are querying for you could potentially hit
>>> tombstones in other SSTables that contain the same "id". As mentioned
>>> previously, you need to add a time bucket to your partitioning key and
>>> definitely use DTCS/TWCS.
>>>
>>>
>>> As I mentioned previously, the UI only queries recent data, e.g., the
>>> past hour, past two hours, past day, past week. The UI does not query for
>>> anything older than the TTL which is 7 days. My understanding and
>>> expectation was that Cassandra would only scan live cells. The UI is a
>>> separate application that I do not maintain, so I am not 100% certain about
>>> the queries. I have been told that it does not query for anything older
>>> than 7 days.
>>>
>>> On Sun, Jan 29, 2017 at 4:14 AM, kurt greaves 
>>> wrote:
>>>
>>>
>>> Your partitioning key is text. If you have multiple entries per id you
>>> are likely hitting older cells that have expired. Descending only affects
>>> how the data is stored on disk, if you have to read the whole partition to
>>> find whichever time you are querying for you could potentially hit
>>> tombstones in other SSTables that contain the same "id". As mentioned
>>> previously, you need to add a time bucket to your partitioning key and
>>> definitely use DTCS/TWCS.
>>>
>>>
>>>
>>>
>>>
>>> --
>>>
>>> - John
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>


-- 

- John

Re: Time series data model and tombstones

[DuyHai Doan]

Ok so give it a try with TWCS. Since STCS does not sort data based on
timestamp, your wide partition may span over multiple SSTables and inside
each SSTable, old data (+ tombstones) may sit on the same partition as
newer data.

When reading by slice, even if you request for fresh data, Cassandra has to
scan over a lot tombstones to fetch the correct range of data thus your
issue

On Sun, Jan 29, 2017 at 8:19 PM, John Sanda  wrote:

> It was with STCS. It was on a 2.x version before TWCS was available.
>
> On Sun, Jan 29, 2017 at 10:58 AM DuyHai Doan  wrote:
>
>> Did you get this Overwhelming tombstonne behavior with STCS or with TWCS ?
>>
>> If you're using DTCS, beware of its weird behavior and tricky
>> configuration.
>>
>> On Sun, Jan 29, 2017 at 3:52 PM, John Sanda  wrote:
>>
>> Your partitioning key is text. If you have multiple entries per id you
>> are likely hitting older cells that have expired. Descending only affects
>> how the data is stored on disk, if you have to read the whole partition to
>> find whichever time you are querying for you could potentially hit
>> tombstones in other SSTables that contain the same "id". As mentioned
>> previously, you need to add a time bucket to your partitioning key and
>> definitely use DTCS/TWCS.
>>
>>
>> As I mentioned previously, the UI only queries recent data, e.g., the
>> past hour, past two hours, past day, past week. The UI does not query for
>> anything older than the TTL which is 7 days. My understanding and
>> expectation was that Cassandra would only scan live cells. The UI is a
>> separate application that I do not maintain, so I am not 100% certain about
>> the queries. I have been told that it does not query for anything older
>> than 7 days.
>>
>> On Sun, Jan 29, 2017 at 4:14 AM, kurt greaves 
>> wrote:
>>
>>
>> Your partitioning key is text. If you have multiple entries per id you
>> are likely hitting older cells that have expired. Descending only affects
>> how the data is stored on disk, if you have to read the whole partition to
>> find whichever time you are querying for you could potentially hit
>> tombstones in other SSTables that contain the same "id". As mentioned
>> previously, you need to add a time bucket to your partitioning key and
>> definitely use DTCS/TWCS.
>>
>>
>>
>>
>>
>> --
>>
>> - John
>>
>>
>>
>>
>>
>>
>>
>>

Re: Time series data model and tombstones

[John Sanda]

It was with STCS. It was on a 2.x version before TWCS was available.

On Sun, Jan 29, 2017 at 10:58 AM DuyHai Doan  wrote:

> Did you get this Overwhelming tombstonne behavior with STCS or with TWCS ?
>
> If you're using DTCS, beware of its weird behavior and tricky
> configuration.
>
> On Sun, Jan 29, 2017 at 3:52 PM, John Sanda  wrote:
>
> Your partitioning key is text. If you have multiple entries per id you are
> likely hitting older cells that have expired. Descending only affects how
> the data is stored on disk, if you have to read the whole partition to find
> whichever time you are querying for you could potentially hit tombstones in
> other SSTables that contain the same "id". As mentioned previously, you
> need to add a time bucket to your partitioning key and definitely use
> DTCS/TWCS.
>
>
> As I mentioned previously, the UI only queries recent data, e.g., the past
> hour, past two hours, past day, past week. The UI does not query for
> anything older than the TTL which is 7 days. My understanding and
> expectation was that Cassandra would only scan live cells. The UI is a
> separate application that I do not maintain, so I am not 100% certain about
> the queries. I have been told that it does not query for anything older
> than 7 days.
>
> On Sun, Jan 29, 2017 at 4:14 AM, kurt greaves 
> wrote:
>
>
> Your partitioning key is text. If you have multiple entries per id you are
> likely hitting older cells that have expired. Descending only affects how
> the data is stored on disk, if you have to read the whole partition to find
> whichever time you are querying for you could potentially hit tombstones in
> other SSTables that contain the same "id". As mentioned previously, you
> need to add a time bucket to your partitioning key and definitely use
> DTCS/TWCS.
>
>
>
>
>
> --
>
> - John
>
>
>
>
>
>
>
>

Re: Time series data model and tombstones

[DuyHai Doan]

Did you get this Overwhelming tombstonne behavior with STCS or with TWCS ?

If you're using DTCS, beware of its weird behavior and tricky configuration.

On Sun, Jan 29, 2017 at 3:52 PM, John Sanda  wrote:

> Your partitioning key is text. If you have multiple entries per id you are
>> likely hitting older cells that have expired. Descending only affects how
>> the data is stored on disk, if you have to read the whole partition to find
>> whichever time you are querying for you could potentially hit tombstones in
>> other SSTables that contain the same "id". As mentioned previously, you
>> need to add a time bucket to your partitioning key and definitely use
>> DTCS/TWCS.
>
>
> As I mentioned previously, the UI only queries recent data, e.g., the past
> hour, past two hours, past day, past week. The UI does not query for
> anything older than the TTL which is 7 days. My understanding and
> expectation was that Cassandra would only scan live cells. The UI is a
> separate application that I do not maintain, so I am not 100% certain about
> the queries. I have been told that it does not query for anything older
> than 7 days.
>
> On Sun, Jan 29, 2017 at 4:14 AM, kurt greaves 
> wrote:
>
>>
>> Your partitioning key is text. If you have multiple entries per id you
>> are likely hitting older cells that have expired. Descending only affects
>> how the data is stored on disk, if you have to read the whole partition to
>> find whichever time you are querying for you could potentially hit
>> tombstones in other SSTables that contain the same "id". As mentioned
>> previously, you need to add a time bucket to your partitioning key and
>> definitely use DTCS/TWCS.
>>
>
>
>
> --
>
> - John
>

Re: Time series data model and tombstones

[John Sanda]

>
> Your partitioning key is text. If you have multiple entries per id you are
> likely hitting older cells that have expired. Descending only affects how
> the data is stored on disk, if you have to read the whole partition to find
> whichever time you are querying for you could potentially hit tombstones in
> other SSTables that contain the same "id". As mentioned previously, you
> need to add a time bucket to your partitioning key and definitely use
> DTCS/TWCS.


As I mentioned previously, the UI only queries recent data, e.g., the past
hour, past two hours, past day, past week. The UI does not query for
anything older than the TTL which is 7 days. My understanding and
expectation was that Cassandra would only scan live cells. The UI is a
separate application that I do not maintain, so I am not 100% certain about
the queries. I have been told that it does not query for anything older
than 7 days.

On Sun, Jan 29, 2017 at 4:14 AM, kurt greaves  wrote:

>
> Your partitioning key is text. If you have multiple entries per id you are
> likely hitting older cells that have expired. Descending only affects how
> the data is stored on disk, if you have to read the whole partition to find
> whichever time you are querying for you could potentially hit tombstones in
> other SSTables that contain the same "id". As mentioned previously, you
> need to add a time bucket to your partitioning key and definitely use
> DTCS/TWCS.
>



-- 

- John

Re: Time series data model and tombstones

[kurt greaves]

Your partitioning key is text. If you have multiple entries per id you are
likely hitting older cells that have expired. Descending only affects how
the data is stored on disk, if you have to read the whole partition to find
whichever time you are querying for you could potentially hit tombstones in
other SSTables that contain the same "id". As mentioned previously, you
need to add a time bucket to your partitioning key and definitely use
DTCS/TWCS.

Re: Time series data model and tombstones

[Benjamin Roth]

Maybe trace your queries to see what's happening in detail.

Am 28.01.2017 21:32 schrieb "John Sanda" :

Thanks for the response. This version of the code is using STCS.
gc_grace_seconds was set to one day and then I changed it to zero since RF
= 1. I understand that expired data will still generate tombstones and that
STCS is not the best. More recent versions of the code use DTCS, and we'll
be switching over to TWCS shortly. The suggestions raised are excellent
ones, but I tend to think of them as optimizations that might not address
my issue which I think may be 1) a problem with my data model, 2) problem
with the queries used or 3) some misunderstanding of Cassandra performs
range scans.

I am doing append-only writes. There is no out of order data. There are no
deletes, just TTLs. Data is stored on disk in descending order, and queries
access recent data and never query past the TTL of seven days. Given this I
would not except to be reading tombstones, certainly not the large numbers
that I am seeing.

On Sat, Jan 28, 2017 at 12:15 PM, Jonathan Haddad  wrote:

> Since you didn't specify a compaction strategy I'm guessing you're using
> STCS. Your TTL'ed data is becoming a tombstone. TWCS is a better strategy
> for this type of workload.
> On Sat, Jan 28, 2017 at 8:30 AM John Sanda  wrote:
>
>> I have a time series data model that is basically:
>>
>> CREATE TABLE metrics (
>> id text,
>> time timeuuid,
>> value double,
>> PRIMARY KEY (id, time)
>> ) WITH CLUSTERING ORDER BY (time DESC);
>>
>> I do append-only writes, no deletes, and use a TTL of seven days. Data
>> points are written every seconds. The UI queries data for the past hour,
>> two hours, day, or week. The UI refreshes and executes queries every 30
>> seconds. In one test environment I am seeing lots of tombstone threshold
>> warnings and Cassandra has even OOME'd. Since I am storing data in
>> descending order and always query for recent data, I do not understand why
>> I am running into this problem.
>>
>> I know that it is recommended to do some date partitioning in part to
>> ensure partitions do not grow too large. I already have some changes in
>> place to partition by day.. Before I make those changes I want to
>> understand why I am scanning so many tombstones so that I can be more
>> confident that the date partitioning changes will help.
>>
>> Thanks
>>
>> - John
>>
>


-- 

- John

Re: Time series data model and tombstones

[John Sanda]

Thanks for the response. This version of the code is using STCS.
gc_grace_seconds was set to one day and then I changed it to zero since RF
= 1. I understand that expired data will still generate tombstones and that
STCS is not the best. More recent versions of the code use DTCS, and we'll
be switching over to TWCS shortly. The suggestions raised are excellent
ones, but I tend to think of them as optimizations that might not address
my issue which I think may be 1) a problem with my data model, 2) problem
with the queries used or 3) some misunderstanding of Cassandra performs
range scans.

I am doing append-only writes. There is no out of order data. There are no
deletes, just TTLs. Data is stored on disk in descending order, and queries
access recent data and never query past the TTL of seven days. Given this I
would not except to be reading tombstones, certainly not the large numbers
that I am seeing.

On Sat, Jan 28, 2017 at 12:15 PM, Jonathan Haddad  wrote:

> Since you didn't specify a compaction strategy I'm guessing you're using
> STCS. Your TTL'ed data is becoming a tombstone. TWCS is a better strategy
> for this type of workload.
> On Sat, Jan 28, 2017 at 8:30 AM John Sanda  wrote:
>
>> I have a time series data model that is basically:
>>
>> CREATE TABLE metrics (
>> id text,
>> time timeuuid,
>> value double,
>> PRIMARY KEY (id, time)
>> ) WITH CLUSTERING ORDER BY (time DESC);
>>
>> I do append-only writes, no deletes, and use a TTL of seven days. Data
>> points are written every seconds. The UI queries data for the past hour,
>> two hours, day, or week. The UI refreshes and executes queries every 30
>> seconds. In one test environment I am seeing lots of tombstone threshold
>> warnings and Cassandra has even OOME'd. Since I am storing data in
>> descending order and always query for recent data, I do not understand why
>> I am running into this problem.
>>
>> I know that it is recommended to do some date partitioning in part to
>> ensure partitions do not grow too large. I already have some changes in
>> place to partition by day.. Before I make those changes I want to
>> understand why I am scanning so many tombstones so that I can be more
>> confident that the date partitioning changes will help.
>>
>> Thanks
>>
>> - John
>>
>


-- 

- John

Re: Time series data model and tombstones

[Jonathan Haddad]

Since you didn't specify a compaction strategy I'm guessing you're using
STCS. Your TTL'ed data is becoming a tombstone. TWCS is a better strategy
for this type of workload.
On Sat, Jan 28, 2017 at 8:30 AM John Sanda  wrote:

> I have a time series data model that is basically:
>
> CREATE TABLE metrics (
> id text,
> time timeuuid,
> value double,
> PRIMARY KEY (id, time)
> ) WITH CLUSTERING ORDER BY (time DESC);
>
> I do append-only writes, no deletes, and use a TTL of seven days. Data
> points are written every seconds. The UI queries data for the past hour,
> two hours, day, or week. The UI refreshes and executes queries every 30
> seconds. In one test environment I am seeing lots of tombstone threshold
> warnings and Cassandra has even OOME'd. Since I am storing data in
> descending order and always query for recent data, I do not understand why
> I am running into this problem.
>
> I know that it is recommended to do some date partitioning in part to
> ensure partitions do not grow too large. I already have some changes in
> place to partition by day.. Before I make those changes I want to
> understand why I am scanning so many tombstones so that I can be more
> confident that the date partitioning changes will help.
>
> Thanks
>
> - John
>

Re: Time series data model and tombstones

[DuyHai Doan]

When the data expired (after TTL of 7 days), at the next compaction they
are transformed into tombstonnes and will still stay there during
gc_grace_seconds. After that, they (the tombstonnes) will be completely
removed at the next compaction, if there is any ...

So doing some maths, supposing that you have let gc_grace_seconds to its
default value of 10 days then you'll have tombstonnes for 10 days worth of
data before they got eventually removed...

What is your compaction strategy ? I strongly suggest

1) Setting TTL directly as the table property (ALTER TABLE) instead of
setting it at query level (INSERT INTO ... USING TTL). When setting TTL at
table level, Cassandra can perform some optimization and drop entirely some
SSTable and don't even bother compact them

2) Use TimeWindowCompactionStrategy and tune it properly to accomodate your
workload



On Sat, Jan 28, 2017 at 5:30 PM, John Sanda  wrote:

> I have a time series data model that is basically:
>
> CREATE TABLE metrics (
> id text,
> time timeuuid,
> value double,
> PRIMARY KEY (id, time)
> ) WITH CLUSTERING ORDER BY (time DESC);
>
> I do append-only writes, no deletes, and use a TTL of seven days. Data
> points are written every seconds. The UI queries data for the past hour,
> two hours, day, or week. The UI refreshes and executes queries every 30
> seconds. In one test environment I am seeing lots of tombstone threshold
> warnings and Cassandra has even OOME'd. Since I am storing data in
> descending order and always query for recent data, I do not understand why
> I am running into this problem.
>
> I know that it is recommended to do some date partitioning in part to
> ensure partitions do not grow too large. I already have some changes in
> place to partition by day.. Before I make those changes I want to
> understand why I am scanning so many tombstones so that I can be more
> confident that the date partitioning changes will help.
>
> Thanks
>
> - John
>

Re: time series data model

[kurt Greaves]

On 20 October 2016 at 09:29, wxn...@zjqunshuo.com 
wrote:

> I do need to align the time windows to day bucket to prevent one row
> become too big, and event_time is timestamp since unix epoch. If I use
> bigint as type of event_time, can I do queries as you mentioned?


Yes.

Kurt Greaves
k...@instaclustr.com
www.instaclustr.com

Re: time series data model

[wxn...@zjqunshuo.com]

Hi Kurt,
I do need to align the time windows to day bucket to prevent one row become too 
big, and event_time is timestamp since unix epoch. If I use bigint as type of 
event_time, can I do queries as you mentioned?

-Simon Wu
 
From: kurt Greaves
Date: 2016-10-20 16:18
To: user
Subject: Re: time series data model
If event_time is timestamps since unix epoch you 1. may want to use the 
in-built timestamps type, and 2. order by event_time DESC. 2 applies if you 
want to do queries such as "select * from eventdata where ... and event_time > 
x" (i.e; get latest events).

Other than that your model seems workable, I assume you're using DTCS/TWCS, and 
aligning the time windows to your day bucket. (If not you should do that)

Kurt Greaves
k...@instaclustr.com
www.instaclustr.com

On 20 October 2016 at 07:29, wxn...@zjqunshuo.com <wxn...@zjqunshuo.com> wrote:
Hi All,
I'm trying to migrate my time series data which is GPS trace from mysql to C*. 
I want a wide row to hold one day data. I designed the data model as below. 
Please help to see if there is any problem. Any suggestion is appreciated.

Table Model:
CREATE TABLE cargts.eventdata (
deviceid int,
date int,
event_time bigint,
position text,
PRIMARY KEY ((deviceid, date), event_time)
)

A slice of data:
cqlsh:cargts> SELECT * FROM eventdata WHERE deviceid =186628 and date = 
20160928 LIMIT 10;

 deviceid | date | event_time| position
--+--+---+-
   186628 | 20160928 | 1474992002000 |  
{"latitude":30.343443936386247,"longitude":120.08751351828943,"speed":41,"heading":48}
   186628 | 20160928 | 1474992012000 |   
{"latitude":30.34409508979662,"longitude":120.08840022183352,"speed":45,"heading":53}
   186628 | 20160928 | 1474992022000 |   
{"latitude":30.34461639856887,"longitude":120.08946100336443,"speed":28,"heading":65}
   186628 | 20160928 | 1474992032000 |   
{"latitude":30.34469478717028,"longitude":120.08973154015409,"speed":11,"heading":67}
   186628 | 20160928 | 1474992042000 |   
{"latitude":30.34494998929474,"longitude":120.09027263811151,"speed":19,"heading":47}
   186628 | 20160928 | 1474992052000 | 
{"latitude":30.346057349126617,"longitude":120.08967091817931,"speed":41,"heading":323}
   186628 | 20160928 | 1474992062000 |
{"latitude":30.346997145708,"longitude":120.08883508853253,"speed":52,"heading":323}
   186628 | 20160928 | 1474992072000 | 
{"latitude":30.348131044340988,"longitude":120.08774702315581,"speed":65,"heading":321}
   186628 | 20160928 | 1474992082000 | 
{"latitude":30.349438164412838,"longitude":120.08652612959328,"speed":68,"heading":322}

-Simon Wu

Re: time series data model

[wxn...@zjqunshuo.com]

Thank you Kurt, I thought the one column which was identified by the compsite 
key(deviceId+date+event_time) can hold only one value, so I packaged all info 
into one JSON. Maybe I'm wrong. I rewrite the table as below.

CREATE TABLE cargts.eventdata (
deviceid int,
date int,
event_time bigint,
heading int,
lat decimal,
lon decimal,
speed int,
PRIMARY KEY ((deviceid, date), event_time)
)

cqlsh:cargts> select * from eventdata;

 deviceid | date | event_time| heading | lat   | lon| speed
--+--+---+-+---++---
   186628 | 20160928 | 1474992002005 |  48 | 30.343443 | 120.087514 |41

-Simon Wu

From: kurt Greaves
Date: 2016-10-20 16:23
To: user
Subject: Re: time series data model
Ah didn't pick up on that but looks like he's storing JSON within position. Is 
there any strong reason for this or as Vladimir mentioned can you store the 
fields under "position" in separate columns?

Kurt Greaves
k...@instaclustr.com
www.instaclustr.com

On 20 October 2016 at 08:17, Vladimir Yudovin <vla...@winguzone.com> wrote:
Hi Simon,

Why position is text and not float? Text takes much more place.
Also speed and headings can be calculated basing on latest positions, so you 
can also save them. If you really need it in data base you can save them as 
floats, or compose single float value like speed.heading: 41.173 (or opposite, 
heading.speed) and save column storage overhead.


Best regards, Vladimir Yudovin, 
Winguzone - Hosted Cloud Cassandra
Launch your cluster in minutes.


 On Thu, 20 Oct 2016 03:29:16 -0400<wxn...@zjqunshuo.com> wrote 

Hi All,
I'm trying to migrate my time series data which is GPS trace from mysql to C*. 
I want a wide row to hold one day data. I designed the data model as below. 
Please help to see if there is any problem. Any suggestion is appreciated.

Table Model:
CREATE TABLE cargts.eventdata (
deviceid int,
date int,
event_time bigint,
position text,
PRIMARY KEY ((deviceid, date), event_time)
)

A slice of data:
cqlsh:cargts> SELECT * FROM eventdata WHERE deviceid =186628 and date = 
20160928 LIMIT 10;

 deviceid | date | event_time| position
--+--+---+-
   186628 | 20160928 | 1474992002000 |  
{"latitude":30.343443936386247,"longitude":120.08751351828943,"speed":41,"heading":48}
   186628 | 20160928 | 1474992012000 |   
{"latitude":30.34409508979662,"longitude":120.08840022183352,"speed":45,"heading":53}
   186628 | 20160928 | 1474992022000 |   
{"latitude":30.34461639856887,"longitude":120.08946100336443,"speed":28,"heading":65}
   186628 | 20160928 | 1474992032000 |   
{"latitude":30.34469478717028,"longitude":120.08973154015409,"speed":11,"heading":67}
   186628 | 20160928 | 1474992042000 |   
{"latitude":30.34494998929474,"longitude":120.09027263811151,"speed":19,"heading":47}
   186628 | 20160928 | 1474992052000 | 
{"latitude":30.346057349126617,"longitude":120.08967091817931,"speed":41,"heading":323}
   186628 | 20160928 | 1474992062000 |
{"latitude":30.346997145708,"longitude":120.08883508853253,"speed":52,"heading":323}
   186628 | 20160928 | 1474992072000 | 
{"latitude":30.348131044340988,"longitude":120.08774702315581,"speed":65,"heading":321}
   186628 | 20160928 | 1474992082000 | 
{"latitude":30.349438164412838,"longitude":120.08652612959328,"speed":68,"heading":322}

-Simon Wu

Re: time series data model

[kurt Greaves]

If event_time is timestamps since unix epoch you 1. may want to use the
in-built timestamps type, and 2. order by event_time DESC. 2 applies if you
want to do queries such as "select * from eventdata where ... and
event_time > x" (i.e; get latest events).

Other than that your model seems workable, I assume you're using DTCS/TWCS,
and aligning the time windows to your day bucket. (If not you should do
that)

Kurt Greaves
k...@instaclustr.com
www.instaclustr.com

On 20 October 2016 at 07:29, wxn...@zjqunshuo.com 
wrote:

> Hi All,
> I'm trying to migrate my time series data which is GPS trace from mysql to
> C*. I want a wide row to hold one day data. I designed the data model as
> below. Please help to see if there is any problem. Any suggestion is
> appreciated.
>
> Table Model:
> CREATE TABLE cargts.eventdata (
> deviceid int,
> date int,
> event_time bigint,
> position text,
> PRIMARY KEY ((deviceid, date), event_time)
> )
>
> A slice of data:
> cqlsh:cargts> SELECT * FROM eventdata WHERE deviceid =
> 186628 and date = 20160928 LIMIT 10;
>
>  deviceid | date | event_time| position
> --+--+---+--
> ---
>186628 | 20160928 | 1474992002000 |  {"latitude":
> 30.343443936386247,"longitude":120.08751351828943,"speed":41,"heading":48}
>186628 | 20160928 | 1474992012000 |   {"latitude":
> 30.34409508979662,"longitude":120.08840022183352,"speed":45,"heading":53}
>186628 | 20160928 | 1474992022000 |   {"latitude":
> 30.34461639856887,"longitude":120.08946100336443,"speed":28,"heading":65}
>186628 | 20160928 | 1474992032000 |   {"latitude":
> 30.34469478717028,"longitude":120.08973154015409,"speed":11,"heading":67}
>186628 | 20160928 | 1474992042000 |   {"latitude":
> 30.34494998929474,"longitude":120.09027263811151,"speed":19,"heading":47}
>186628 | 20160928 | 1474992052000 | {"latitude":
> 30.346057349126617,"longitude":120.08967091817931,"speed":
> 41,"heading":323}
>186628 | 20160928 | 1474992062000 |{"latitude"
> :30.346997145708,"longitude":120.08883508853253,"speed":52,"heading":323}
>186628 | 20160928 | 1474992072000 | {"latitude":
> 30.348131044340988,"longitude":120.08774702315581,"speed":
> 65,"heading":321}
>186628 | 20160928 | 1474992082000 | {"latitude":
> 30.349438164412838,"longitude":120.08652612959328,"speed":
> 68,"heading":322}
>
> -Simon Wu
>

Re: time series data model

[kurt Greaves]

Ah didn't pick up on that but looks like he's storing JSON within position.
Is there any strong reason for this or as Vladimir mentioned can you store
the fields under "position" in separate columns?

Kurt Greaves
k...@instaclustr.com
www.instaclustr.com

On 20 October 2016 at 08:17, Vladimir Yudovin  wrote:

> Hi Simon,
>
> Why *position *is text and not float? Text takes much more place.
> Also speed and headings can be calculated basing on latest positions, so
> you can also save them. If you really need it in data base you can save
> them as floats, or compose single float value like speed.heading: 41.173
> (or opposite, heading.speed) and save column storage overhead.
>
>
> Best regards, Vladimir Yudovin,
>
> *Winguzone  - Hosted Cloud
> CassandraLaunch your cluster in minutes.*
>
>
>  On Thu, 20 Oct 2016 03:29:16 -0400* >* wrote 
>
> Hi All,
> I'm trying to migrate my time series data which is GPS trace from mysql to
> C*. I want a wide row to hold one day data. I designed the data model as
> below. Please help to see if there is any problem. Any suggestion is
> appreciated.
>
> Table Model:
> CREATE TABLE cargts.eventdata (
> deviceid int,
> date int,
> event_time bigint,
> position text,
> PRIMARY KEY ((deviceid, date), event_time)
> )
>
> A slice of data:
> cqlsh:cargts> SELECT * FROM eventdata WHERE deviceid =
> 186628 and date = 20160928 LIMIT 10;
>
>  deviceid | date | event_time| position
> --+--+---+--
> ---
>186628 | 20160928 | 1474992002000 |  {"latitude":
> 30.343443936386247,"longitude":120.08751351828943,"speed":41,"heading":48}
>186628 | 20160928 | 1474992012000 |   {"latitude":
> 30.34409508979662,"longitude":120.08840022183352,"speed":45,"heading":53}
>186628 | 20160928 | 1474992022000 |   {"latitude":
> 30.34461639856887,"longitude":120.08946100336443,"speed":28,"heading":65}
>186628 | 20160928 | 1474992032000 |   {"latitude":
> 30.34469478717028,"longitude":120.08973154015409,"speed":11,"heading":67}
>186628 | 20160928 | 1474992042000 |   {"latitude":
> 30.34494998929474,"longitude":120.09027263811151,"speed":19,"heading":47}
>186628 | 20160928 | 1474992052000 | {"latitude":
> 30.346057349126617,"longitude":120.08967091817931,"speed":
> 41,"heading":323}
>186628 | 20160928 | 1474992062000 |{"latitude"
> :30.346997145708,"longitude":120.08883508853253,"speed":52,"heading":323}
>186628 | 20160928 | 1474992072000 | {"latitude":
> 30.348131044340988,"longitude":120.08774702315581,"speed":
> 65,"heading":321}
>186628 | 20160928 | 1474992082000 | {"latitude":
> 30.349438164412838,"longitude":120.08652612959328,"speed":
> 68,"heading":322}
>
> -Simon Wu
>
>
>

Re: time series data model

[Vladimir Yudovin]

Hi Simon,

Why position is text and not float? Text takes much more place.
Also speed and headings can be calculated basing on latest positions, so you 
can also save them. If you really need it in data base you can save them as 
floats, or compose single float value like speed.heading: 41.173 (or opposite, 
heading.speed) and save column storage overhead.



Best regards, Vladimir Yudovin, 

Winguzone - Hosted Cloud Cassandra
Launch your cluster in minutes.





 On Thu, 20 Oct 2016 03:29:16 -0400wxn...@zjqunshuo.com wrote 




Hi All,

I'm trying to migrate my time series data which is GPS trace from mysql to C*. 
I want a wide row to hold one day data. I designed the data model as below. 
Please help to see if there is any problem. Any suggestion is appreciated.



Table Model:

CREATE TABLE cargts.eventdata (
deviceid int,
date int,
event_time bigint,
position text,
PRIMARY KEY ((deviceid, date), event_time)
)


A slice of data:

cqlsh:cargts SELECT * FROM eventdata WHERE deviceid =186628 and date = 
20160928 LIMIT 10;

 deviceid | date | event_time| position
--+--+---+-
   186628 | 20160928 | 1474992002000 |  
{"latitude":30.343443936386247,"longitude":120.08751351828943,"speed":41,"heading":48}
   186628 | 20160928 | 1474992012000 |   
{"latitude":30.34409508979662,"longitude":120.08840022183352,"speed":45,"heading":53}
   186628 | 20160928 | 1474992022000 |   
{"latitude":30.34461639856887,"longitude":120.08946100336443,"speed":28,"heading":65}
   186628 | 20160928 | 1474992032000 |   
{"latitude":30.34469478717028,"longitude":120.08973154015409,"speed":11,"heading":67}
   186628 | 20160928 | 1474992042000 |   
{"latitude":30.34494998929474,"longitude":120.09027263811151,"speed":19,"heading":47}
   186628 | 20160928 | 1474992052000 | 
{"latitude":30.346057349126617,"longitude":120.08967091817931,"speed":41,"heading":323}
   186628 | 20160928 | 1474992062000 |
{"latitude":30.346997145708,"longitude":120.08883508853253,"speed":52,"heading":323}
   186628 | 20160928 | 1474992072000 | 
{"latitude":30.348131044340988,"longitude":120.08774702315581,"speed":65,"heading":321}
   186628 | 20160928 | 1474992082000 | 
{"latitude":30.349438164412838,"longitude":120.08652612959328,"speed":68,"heading":322}


-Simon Wu

Re: Time-series data model

[Jean-Pierre Bergamin]

Am 14.04.2010 15:22, schrieb Ted Zlatanov:

On Wed, 14 Apr 2010 15:02:29 +0200 Jean-Pierre Bergaminja...@ractive.ch  
wrote:

JB  The metrics are stored together with a timestamp. The queries we want to
JB  perform are:
JB   * The last value of a specific metric of a device
JB   * The values of a specific metric of a device between two timestamps t1 
and
JB  t2

Make your key devicename-metricname-MMDD-HHMM (with whatever time
sharding makes sense to you; I use UTC by-hours and by-day in my
environment).  Then your supercolumn is the collection time as a
LongType and your columns inside the supercolumn can express the metric
in detail (collector agent, detailed breakdown, etc.).
   
Just for my understanding. What is time sharding? I couldn't find an 
explanation somewhere. Do you mean that the time-series data is rolled 
up in 5 minues, 1 hour, 1 day etc. slices?


So this would be defined as:
ColumnFamily Name=measurements ColumnType=Super 
CompareWith=UTF8Type  CompareSubcolumnsWith=LongType /


So when i want to read all values of one metric between two timestamps 
t0 and t1, I'd have to read the supercolumns that match a key range 
(device1:metric1:t0 - device1:metric1:t1) and then all the supercolumns 
for this key?



Regards
James

Re: Time-series data model

[Dan Di Spaltro]

This is actually fairly similar to how we store metrics at Cloudkick.
Below has a much more in depth explanation of some of that

https://www.cloudkick.com/blog/2010/mar/02/4_months_with_cassandra/

So we store each natural point in the NumericArchive table.

ColumnFamily CompareWith=LongType
  Name=NumericArchive /

ColumnFamily CompareWith=LongType Name=Rollup5m
ColumnType=Super CompareSubcolumnsWith=BytesType /
ColumnFamily CompareWith=LongType Name=Rollup20m
ColumnType=Super CompareSubcolumnsWith=BytesType /
ColumnFamily CompareWith=LongType Name=Rollup30m
ColumnType=Super CompareSubcolumnsWith=BytesType /
ColumnFamily CompareWith=LongType Name=Rollup60m
ColumnType=Super CompareSubcolumnsWith=BytesType /
ColumnFamily CompareWith=LongType Name=Rollup4h
ColumnType=Super CompareSubcolumnsWith=BytesType /
ColumnFamily CompareWith=LongType Name=Rollup12h
ColumnType=Super CompareSubcolumnsWith=BytesType /
ColumnFamily CompareWith=LongType Name=Rollup1d
ColumnType=Super CompareSubcolumnsWith=BytesType /

our keys look like:
serviceuuid.metric-name

Anyways, this has been working out very well for us.

2010/4/15 Ted Zlatanov t...@lifelogs.com:
 On Thu, 15 Apr 2010 11:27:47 +0200 Jean-Pierre Bergamin ja...@ractive.ch 
 wrote:

 JB Am 14.04.2010 15:22, schrieb Ted Zlatanov:
 On Wed, 14 Apr 2010 15:02:29 +0200 Jean-Pierre Bergaminja...@ractive.ch 
  wrote:

 JB The metrics are stored together with a timestamp. The queries we want to
 JB perform are:
 JB * The last value of a specific metric of a device
 JB * The values of a specific metric of a device between two timestamps t1 
 and
 JB t2

 Make your key devicename-metricname-MMDD-HHMM (with whatever time
 sharding makes sense to you; I use UTC by-hours and by-day in my
 environment).  Then your supercolumn is the collection time as a
 LongType and your columns inside the supercolumn can express the metric
 in detail (collector agent, detailed breakdown, etc.).

 JB Just for my understanding. What is time sharding? I couldn't find an
 JB explanation somewhere. Do you mean that the time-series data is rolled
 JB up in 5 minues, 1 hour, 1 day etc. slices?

 Yes.  The usual meaning of shard in RDBMS world is to segment your
 database by some criteria, e.g. US vs. Europe in Amazon AWS because
 their data centers are laid out so.  I was taking a linguistic shortcut
 to mean break down your rows by some convenient criteria.  You can
 actually set up your Partitioner in Cassandra to literally shard your
 keyspace rows based on the key, but I just meant slice in my note.

 JB So this would be defined as:
 JB ColumnFamily Name=measurements ColumnType=Super
 JB CompareWith=UTF8Type  CompareSubcolumnsWith=LongType /

 JB So when i want to read all values of one metric between two timestamps
 JB t0 and t1, I'd have to read the supercolumns that match a key range
 JB (device1:metric1:t0 - device1:metric1:t1) and then all the
 JB supercolumns for this key?

 Yes.  This is a single multiget if you can construct the key range
 explicitly.  Cassandra loads a lot of this in memory already and filters
 it after the fact, that's why it pays to slice your keys and to stitch
 them together on the client side if you have to go across a time
 boundary.  You'll also get better key load balancing with deeper slicing
 if you use the randomizing partitioner.

 In the result set, you'll get each matching supercolumn with all the
 columns inside it.  You may have to page through supercolumns.

 Ted





-- 
Dan Di Spaltro

Re: Time-series data model

[Zhiguo Zhang]

first of all I am a new bee by Non-SQL. I try write down my opinions as
references:

If I were you, I will use 2 columnfamilys:

1.CF,  key is devices
2.CF,  key is timeuuid

how do u think about that?

Mike


On Wed, Apr 14, 2010 at 3:02 PM, Jean-Pierre Bergamin ja...@ractive.chwrote:

 Hello everyone

 We are currently evaluating a new DB system (replacing MySQL) to store
 massive amounts of time-series data. The data are various metrics from
 various network and IT devices and systems. Metrics i.e. could be CPU usage
 of the server xy in percent, memory usage of server xy in MB, ping
 response time of server foo in milliseconds, network traffic of router
 bar in MB/s and so on. Different metrics can be collected for different
 devices in different intervals.

 The metrics are stored together with a timestamp. The queries we want to
 perform are:
  * The last value of a specific metric of a device
  * The values of a specific metric of a device between two timestamps t1
 and
 t2

 I stumbled across this blog post which describes a very similar setup with
 Cassandra:
 https://www.cloudkick.com/blog/2010/mar/02/4_months_with_cassandra/
 This post gave me confidence that what we want is definitively doable with
 Cassandra.

 But since I'm just digging into columns and super-columns and their
 families, I still have some problems understanding everything.

 Our data model could look in json'isch notation like this:
 {
 my_server_1: {
cpu_usage: {
{ts: 1271248215, value: 87 },
{ts: 1271248220, value: 34 },
{ts: 1271248225, value: 23 },
{ts: 1271248230, value: 49 }
}
ping_response: {
{ts: 1271248201, value: 0.345 },
{ts: 1271248211, value: 0.423 },
{ts: 1271248221, value: 0.311 },
{ts: 1271248232, value: 0.582 }
}
 }

 my_server_2: {
cpu_usage: {
{ts: 1271248215, value: 23 },
...
}
disk_usage: {
{ts: 1271243451, value: 123445 },
...
}
 }

 my_router_1: {
bytes_in: {
{ts: 1271243451, value: 2452346 },
...
}
bytes_out: {
{ts: 1271243451, value: 13468 },
...
}
errors: {
{ts: 1271243451, value: 24 },
...
}
 }
 }

 What I don't get is how to created the two level hierarchy
 [device][metric].

 Am I right that the devices would be kept in a super column family? The
 ordering of those is not important.

 But the metrics per device are also a super column, where the columns would
 be the metric values ({ts: 1271243451, value: 24 }), isn't it?

 So I'd need a super column in a super column... Hm.
 My brain is definitively RDBMS-damaged and I don't see through columns and
 super-columns yet. :-)

 How could this be modeled in Cassandra?


 Thank you very much
 James

Re: Time-series data model

[Ted Zlatanov]

On Wed, 14 Apr 2010 15:02:29 +0200 Jean-Pierre Bergamin ja...@ractive.ch 
wrote: 

JB The metrics are stored together with a timestamp. The queries we want to
JB perform are:
JB  * The last value of a specific metric of a device
JB  * The values of a specific metric of a device between two timestamps t1 and
JB t2

Make your key devicename-metricname-MMDD-HHMM (with whatever time
sharding makes sense to you; I use UTC by-hours and by-day in my
environment).  Then your supercolumn is the collection time as a
LongType and your columns inside the supercolumn can express the metric
in detail (collector agent, detailed breakdown, etc.).

If you want your clients to discover the available metrics, you may need
to keep an external index.  But from your spec that doesn't seem necessary.

Ted