Loosing atomic updates is a good point, but in my use case its not a problem, since I always overwrite the whole record (no partitial updates).
I’m still not sure if having tombstones vs. empty values / frozen UDTs will have the same results. When I update one row with 10 null columns it will create 10 tombstones. We do OLAP processing of data stored in Cassandra with Spark. When Spark requests range of data, lets say 1000 rows, I can easily hit the 10 000 tombstones threshold. Even if I would not hit the error threshold Spark requests would increase the heap pressure, because tombstones have to be collected and returned to coordinator. Are my assumptions correct ? > On 4 Jan 2019, at 21:15, DuyHai Doan <doanduy...@gmail.com> wrote: > > The idea of storing your data as a single blob can be dangerous. > > Indeed, you loose the ability to perform atomic update on each column. > > In Cassandra, LWW is the rule. Suppose 2 concurrent updates on the same row, > 1st update changes column Firstname (let's say it's a Person record) and 2nd > update changes column Lastname > > Now depending on the timestamp between the 2 updates, you'll have: > > - old Firstname, new Lastname > - new Firstname, old Lastname > > having updates on columns atomically guarantees you to have new Firstname, > new Lastname > > On Fri, Jan 4, 2019 at 8:17 PM Jonathan Haddad <j...@jonhaddad.com > <mailto:j...@jonhaddad.com>> wrote: > Those are two different cases though. It *sounds like* (again, I may be > missing the point) you're trying to overwrite a value with another value. > You're either going to serialize a blob and overwrite a single cell, or > you're going to overwrite all the cells and include a tombstone. > > When you do a read, reading a single tombstone vs a single vs is essentially > the same thing, performance wise. > > In your description you said "~ 20-100 events", and you're overwriting the > event each time, so I don't know how you go to 10K tombstones either. > Compaction will bring multiple tombstones together for a cell in the same way > it compacts multiple values for a single cell. > > I sounds to make like you're taking some advice about tombstones out of > context and trying to apply the advice to a different problem. Again, I > might be misunderstanding what you're doing. > > > On Fri, Jan 4, 2019 at 10:49 AM Tomas Bartalos <tomas.barta...@gmail.com > <mailto:tomas.barta...@gmail.com>> wrote: > Hello Jon, > > I thought having tombstones is much higher overhead than just overwriting > values. The compaction overhead can be l similar, but I think the read > performance is much worse. > > Tombstones accumulate and hang for 10 days (by default) before they are > eligible for compaction. > > Also we have tombstone warning and error thresholds. If cassandra scans more > than 10 000 tombstones, she will abort the query. > > According to this article: > https://opencredo.com/blogs/cassandra-tombstones-common-issues/ > <https://opencredo.com/blogs/cassandra-tombstones-common-issues/> > > "The cassandra.yaml comments explain in perfectly: “When executing a scan, > within or across a partition, we need to keep the tombstones seen in memory > so we can return them to the coordinator, which will use them to make sure > other replicas also know about the deleted rows. With workloads that generate > a lot of tombstones, this can cause performance problems and even exhaust the > server heap. " > > Regards, > Tomas > > On Fri, 4 Jan 2019, 7:06 pm Jonathan Haddad <j...@jonhaddad.com > <mailto:j...@jonhaddad.com> wrote: > 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 value? > > Jon > > > On Fri, Jan 4, 2019 at 9:57 AM Tomas Bartalos <tomas.barta...@gmail.com > <mailto:tomas.barta...@gmail.com>> wrote: > 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 <doanduy...@gmail.com > <mailto:doanduy...@gmail.com> 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: > > https://github.com/doanduyhai/Achilles/wiki/Insert-Strategy > <https://github.com/doanduyhai/Achilles/wiki/Insert-Strategy> > > Or you can use the Update API that by design only perform update on not null > fields: > https://github.com/doanduyhai/Achilles/wiki/Quick-Reference#updating-all-non-null-fields-for-an-entity > > <https://github.com/doanduyhai/Achilles/wiki/Quick-Reference#updating-all-non-null-fields-for-an-entity> > > > 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 <tomas.barta...@gmail.com > <mailto:tomas.barta...@gmail.com>> 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 <migh...@gmail.com > <mailto:migh...@gmail.com> 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: > https://docs.datastax.com/en/developer/java-driver/3.0/manual/statements/prepared/#parameters-and-binding > > <https://docs.datastax.com/en/developer/java-driver/3.0/manual/statements/prepared/#parameters-and-binding> > Python Driver: > https://www.datastax.com/dev/blog/python-driver-2-6-0-rc1-with-cassandra-2-2-features#distinguishing_between_null_and_unset_values > > <https://www.datastax.com/dev/blog/python-driver-2-6-0-rc1-with-cassandra-2-2-features#distinguishing_between_null_and_unset_values> > Node Driver: > https://docs.datastax.com/en/developer/nodejs-driver/3.5/features/datatypes/nulls/#unset > > <https://docs.datastax.com/en/developer/nodejs-driver/3.5/features/datatypes/nulls/#unset> > On Thu, Dec 27, 2018 at 3:21 PM Durity, Sean R <sean_r_dur...@homedepot.com > <mailto:sean_r_dur...@homedepot.com>> 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 <tomas.barta...@gmail.com > <mailto:tomas.barta...@gmail.com>> > Sent: Thursday, December 27, 2018 9:27 AM > To: user@cassandra.apache.org <mailto:user@cassandra.apache.org> > 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. ignore NULLs > on spark side with “spark.cassandra.output.ignoreNulls=true”. Not good since > this will never overwrite previously inserted event field with “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 > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@cassandra.apache.org > <mailto:user-unsubscr...@cassandra.apache.org> > For additional commands, e-mail: user-h...@cassandra.apache.org > <mailto:user-h...@cassandra.apache.org> > > > ________________________________ > > The information in this Internet Email is confidential and may be legally > privileged. It is intended solely for the addressee. Access to this Email by > anyone else is unauthorized. If you are not the intended recipient, any > disclosure, copying, distribution or any action taken or omitted to be taken > in reliance on it, is prohibited and may be unlawful. When addressed to our > clients any opinions or advice contained in this Email are subject to the > terms and conditions expressed in any applicable governing The Home Depot > terms of business or client engagement letter. 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