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From Jonathan Haddad <>
Subject Re: [EXTERNAL] Howto avoid tombstones when inserting NULL values
Date Fri, 04 Jan 2019 18:05:44 GMT
If you're overwriting values, it really doesn't matter much if it's a
tombstone or any other value, they still need to be compacted and have the
same overhead at read time.

Tombstones are problematic when you try to use Cassandra as a queue (or
something like a queue) and you need to scan over thousands of tombstones
in order to get to the real data.  You're simply overwriting a row and
trying to avoid a single tombstone.

Maybe I'm missing something here.  Why do you think overwriting a single
cell with a tombstone is any worse than overwriting a single cell with a


On Fri, Jan 4, 2019 at 9:57 AM Tomas Bartalos <>

> Hello,
> I beleive your approach is the same as using spark with "
> spark.cassandra.output.ignoreNulls=true"
> This will not cover the situation when a value have to be overwriten with
> null.
> I found one possible solution - change the schema to keep only primary key
> fields and move all other fields to frozen UDT.
> create table (year, month, day, id, frozen<Event>, primary key((year,
> month, day), id) )
> In this way anything that is null inside event doesn't create tombstone,
> since event is serialized to BLOB.
> The penalty is in need of deserializing the whole Event when selecting
> only few columns.
> Can anyone confirm if this is good solution performance wise?
> Thank you,
> On Fri, 4 Jan 2019, 2:20 pm DuyHai Doan < wrote:
>> "The problem is I can't know the combination of set/unset values" -->
>> Just for this requirement, Achilles has a working solution for many years
>> using INSERT_NOT_NULL_FIELDS strategy:
>> Or you can use the Update API that by design only perform update on not
>> null fields:
>> Behind the scene, for each new combination of INSERT INTO table(x,y,z)
>> statement, Achilles will check its prepared statement cache and if the
>> statement does not exist yet, create a new prepared statement and put it
>> into the cache for later re-use for you
>> Disclaiment: I'm the creator of Achilles
>> On Thu, Dec 27, 2018 at 10:21 PM Tomas Bartalos <>
>> wrote:
>>> Hello,
>>> The problem is I can't know the combination of set/unset values. From my
>>> perspective every value should be set. The event from Kafka represents the
>>> complete state of the happening at certain point in time. In my table I
>>> want to store the latest event so the most recent state of the happening
>>> (in this table I don't care about the history). Actually I used wrong
>>> expression since its just the opposite of "incremental update", every event
>>> carries all data (state) for specific point of time.
>>> The event is represented with nested json structure. Top level elements
>>> of the json are table fields with type like text, boolean, timestamp, list
>>> and the nested elements are UDT fields.
>>> Simplified example:
>>> There is a new purchase for the happening, event:
>>> {total_amount: 50, items : [A, B, C, new_item], purchase_time :
>>> '2018-12-27 13:30', specials: null, customer : {... }, fare_amount,...}
>>> I don't know what actually happened for this event, maybe there is a new
>>> item purchased, maybe some customer info have been changed, maybe the
>>> specials have been revoked and I have to reset them. I just need to store
>>> the state as it artived from Kafka, there might already be an event for
>>> this happening saved before, or maybe this is the first one.
>>> BR,
>>> Tomas
>>> On Thu, 27 Dec 2018, 9:36 pm Eric Stevens < wrote:
>>>> Depending on the use case, creating separate prepared statements for
>>>> each combination of set / unset values in large INSERT/UPDATE statements
>>>> may be prohibitive.
>>>> Instead, you can look into driver level support for UNSET values.
>>>> Requires Cassandra 2.2 or later IIRC.
>>>> See:
>>>> Java Driver:
>>>> Python Driver:
>>>> Node Driver:
>>>> On Thu, Dec 27, 2018 at 3:21 PM Durity, Sean R <
>>>>> wrote:
>>>>> You say the events are incremental updates. I am interpreting this to
>>>>> mean only some columns are updated. Others should keep their original
>>>>> values.
>>>>> You are correct that inserting null creates a tombstone.
>>>>> Can you only insert the columns that actually have new values? Just
>>>>> skip the columns with no information. (Make the insert generator a bit
>>>>> smarter.)
>>>>> Create table happening (id text primary key, event text, a text, b
>>>>> text, c text);
>>>>> Insert into table happening (id, event, a, b, c) values
>>>>> ("MainEvent","The most complete info we have right now","Priceless","10
>>>>> pm","Grand Ballroom");
>>>>> -- b changes
>>>>> Insert into happening (id, b) values ("MainEvent","9:30 pm");
>>>>> Sean Durity
>>>>> -----Original Message-----
>>>>> From: Tomas Bartalos <>
>>>>> Sent: Thursday, December 27, 2018 9:27 AM
>>>>> To:
>>>>> Subject: [EXTERNAL] Howto avoid tombstones when inserting NULL values
>>>>> Hello,
>>>>> I’d start with describing my use case and how I’d like to use
>>>>> Cassandra to solve my storage needs.
>>>>> We're processing a stream of events for various happenings. Every
>>>>> event have a unique happening_id.
>>>>> One happening may have many events, usually ~ 20-100 events. I’d like
>>>>> to store only the latest event for the same happening (Event is an
>>>>> incremental update and it contains all up-to date data about happening).
>>>>> Technically the events are streamed from Kafka, processed with Spark
>>>>> an saved to Cassandra.
>>>>> In Cassandra we use upserts (insert with same primary key).  So far so
>>>>> good, however there comes the tombstone...
>>>>> When I’m inserting field with NULL value, Cassandra creates tombstone
>>>>> for this field. As I understood this is due to space efficiency, Cassandra
>>>>> doesn’t have to remember there is a NULL value, she just deletes the
>>>>> respective column and a delete creates a ... tombstone.
>>>>> I was hoping there could be an option to tell Cassandra not to be so
>>>>> space effective and store “unset" info without generating tombstones.
>>>>> Something similar to inserting empty strings instead of null values:
>>>>> CREATE TABLE happening (id text PRIMARY KEY, event text); insert into
>>>>> happening (‘1’, ‘event1’); — tombstone is generated insert
into happening
>>>>> (‘1’, null); — tombstone is not generated insert into happening
(‘1’, '’);
>>>>> Possible solutions:
>>>>> 1. Disable tombstones with gc_grace_seconds = 0 or set to reasonable
>>>>> low value (1 hour ?) . Not good, since phantom data may re-appear 2.
>>>>> NULLs on spark side with “spark.cassandra.output.ignoreNulls=true”.
>>>>> good since this will never overwrite previously inserted event field
>>>>> “empty” one.
>>>>> 3. On inserts with spark, find all NULL values and replace them with
>>>>> “empty” equivalent (empty string for text, 0 for integer). Very inefficient
>>>>> and problematic to find “empty” equivalent for some data types.
>>>>> Until tombstones appeared Cassandra was the right fit for our use
>>>>> case, however now I’m not sure if we’re heading the right direction.
>>>>> Could you please give me some advice how to solve this problem ?
>>>>> Thank you,
>>>>> Tomas
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Jon Haddad
twitter: rustyrazorblade

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