Although I used Cassandra 1.0.X extensively, I'm new to CQL3. Pages such
as http://wiki.apache.org/cassandra/ClientOptionsThrift suggest new
projects should use CQL3.
I'm wondering, however, if there are certain use cases not well covered by
CQL3. Consider the standard timeseries example:
CREATE TABLE timeseries (
event_type text,
insertion_time timestamp,
event blob,
PRIMARY KEY (event_type, insertion_time)
) WITH CLUSTERING ORDER BY (insertion_time DESC);
What happens if I want to store additional information that is shared by
all events in the given series (but that I don't want to include in the row
ID): e.g. the event source, a cached count of the number of events logged
to date, etc.? I might try updating the definition as follows:
CREATE TABLE timeseries (
event_type text,
event_source text,
total_events int,
insertion_time timestamp,
event blob,
PRIMARY KEY (event_type, event_source, total_events, insertion_time)
) WITH CLUSTERING ORDER BY (insertion_time DESC);
Is this not inefficient? When inserting or querying via CQL3, say in
batches of up to 1000 events, won't the type/source/count be repeated 1000
times? Please let me know if I'm misunderstanding something, or if I
should be sticking to Thrift for situations like this involving mixed
static/dynamic data.
Thanks!
