Using Cassandra as an event store

Question

I want to experiment with using Cassandra as an event store in an event sourcing application. My requirements for an event store are quite simple. The event 'schema' would be something like this:

• id: the id of an aggregate root entity
• data: the serialized event data (e.g. JSON)
• timestamp: when the event occurred
• sequence_number: the unique version of the event

I am completely new to Cassandra so forgive me for my ignorance in what I'm about to write. I only have two queries that I'd ever want to run on this data.

1. Give me all events for a given aggregate root id
2. Give me all events for a given aggregate root if where sequence number is > x

My idea is to create a Cassandra table in CQL like this:

CREATE TABLE events (
  id uuid,
  seq_num int,
  data text,
  timestamp timestamp,
  PRIMARY KEY  (id, seq_num) );

Does this seem like a sensible way to model the problem? And, importantly, does using a compound primary key allow me to efficiently perform the queries I specified? Remember that, given the use case, there could be a large number of events (with a different seq_num) for the same aggregate root id.

My specific concern is that the second query is going to be inefficient in some way (I'm thinking about secondary indexes here...)

Answer 1

Your design seem to be well modeled in "cassandra terms". The queries you need are indeed supported in "composite key" tables, you would have something like:

• query 1: select * from events where id = 'id_event';
• query 2: select * from events where id = 'id_event' and seq_num > NUMBER;

I do not think the second query is going to be inefficient, however it may return a lot of elements... if that is the case you could set a "limit" of events to be returned. If that is possible you can use the limit keyword.

Using composite keys seems like a good match for your specific requirements. Using "secondary indexes" do not seem to bring much to the table... unless I miss something in your design/requirements.

HTH.

Answer 2

What you've got is good, except in case of many events for a particular aggregate. One thing you could do is create a static column to hold "next" and "max_sequence". The idea being that the static columns would hold the current max sequence for this partition, and the "artificial id" for the next partition. You could then, say, store 100 or 1000 events per partition. What you've essentially done then is bucketed the events for an aggregate into multiple partitions. This would mean additional overhead for querying and storing, but at the same time protect against unbounded growth. You might even create a lookup for partitions for an aggregate. Really depends on your use case and how "clever" you want it to be.

Answer 3

I've been using Cassandra for a very similar scenario (with 100k+ columns per row) and ended with a model close to yours. I also agree with emgsilva that a secondary index probably won't bring much.

There are three things that turned out to be significant for good performance for our event store: Using composite columns, making sure that the columns are in a nicely sortable order (Cassandra sorts data in rows by columns), and using compact storage if possible.

Note that compact storage means you can only have one value column. Hence, you need to make all other columns part of the key.

For you, the schema would be:

CREATE TABLE events (
    id uuid,
    seq_num int,
    timestamp timestamp,
    data text,
    PRIMARY KEY  (id, seq_num, timestamp))
    WITH COMPACT STORAGE;

Answer 4

Your partition key is too granular, you should create a composite partition key or change it to get better performance for time series modelling. For instance

CREATE TABLE events (
    event_date int,
    id timeuuid,
    seq_num int,
    data text,
    PRIMARY KEY  (event_date, id) );

This way your id will become a clustering column just to guarantee event unicqueness and your partition key (ie. 20160922) can group all events per day. You could change it to month as well. Avoid using uuid use timeuuid instead, it already store timestamp information.

Answer 5

The design seems to be in coherence with how Cassandra would store data, 1st part of your primary key, i.e. your 'id' would be used as to partition the data on separate nodes/v-nodes (depending on how your cluster is configured), this will make fetching data for your 1st query very easy for Cassandra as it has to only touch a single partition, now as per the 2nd part of your key is will be a clustering key i.e. is will specify how the data is ordered inside that partition, which is what your 2nd query is all about. remember, as long as all your data is designed in such a way that each query on a table only touches a single partition, you are good to go. Also if you are worried what the 2nd query is going to return a huge amount of data you can always opt for paging inherently provided by Cassandra for range queries.

Answer 6

I am not agrre with your design to save aggregateroot on eventstore.you chould save domainevent for flexibility . i explain eventdomain is the smaalest grained data that making the change of the state of application.aggregateroot dont mismatch with eventstore it is for data exchange or boundedcontext . when you use domain event you can reconstruct your data even aggregateroot with plolygot modeling .you can manage the model for the need of your client and constraints.So you model graphic for links between domainobject and after that you use neo4j ,in addition you model aggregate model and you use documentdatabase.I mean you have the apportunity to change model and use the convenient persistance engine.it is a difference betwenn polygot data and polygot persistence. in your strategie i understand two ways : if you need eventsourcing you model on domainevent and cassandra database. if you need aggregateroot data or model and no eventsourcing , you use documented database and you can retrive the two queries.

you chould eliminate confusion about domain driven design.