人口稀少的 Frozen 用户定义类型对性能有何影响？

Question

We have a frozen UDT with ~2000 fields as one of the columns in a table. We use this table to implement append-only writes so that the data is auditable and not overwritten.

We are seeing degradation in write performance when only 1 (out of 2000) field in the UDT is populated.

Trying to understand the performance implication of using sparsely populated frozen UDTs. How are UDTs serialized/deserialized internally? Any documentation of this will be highly appreciated.

We tried to gather some metrics from cass session, but couldn't get much information.

edit: We are using the C++ cassandra driver.

Answer 1

What performance are you measuring here? Comparing performance to inserting data using non-UDT columns into a table versus inserting data using both non-UDT columns and UDT-type columns?

a column whose type is a frozen collection (set, map, or list) or UDT can only have its value replaced as a whole. In other words, we can't add, update, or delete individual elements from the collection as we can in non-frozen collection types. So, the frozen keyword can be useful, for example, when we want to protect collections against single-value updates.

For example, in case of the below snippet,

CREATE TYPE IF NOT EXISTS race (
race_title text,
race_date date
);

CREATE TABLE IF NOT EXISTS race_data (
id INT PRIMARY KEY,
races frozen<list<race>>
...
);

the UDT nested in the list is frozen, so the entire list will be read when querying the table.

Since you did not provide "how" you're updating the frozen collection, it is hard to triage why there is a performannce concern here.

References for exploration :

• Freezing collections

Essentially, you will not be able to do an append-only operation with a frozen type as you will always have to perform read-before-write operation for any upserts.