Comments (2)
Can you give some more context on concrete use cases?
- Long-term storage / Archiving - for efficient long-term storage of data, while still keeping metadata information and being much faster queryable / processed on demand. While this is also possible with gzipped json in a way, one can't easily process them, nor does it keep table schema / data type information.
- Integration with Data Engineering Ecosystems: Simplifies data integration with systems like Databricks / Apache Spark, Hadoop and cloud services (AWS S3, Azure Data Lake).
- Machine Learning: enabling quick access to large datasets for feature selection and model training, reducing the load on the database and preventing system slowdowns.
If you're going to use the parquet files with other tools, I'm not sure if having 1 file per shard is appropriate?
One parquet file per shard would be perfect (one might consider even splitting shards for very large ones). I'd say that it is very common to have a dataset composed of multiple parquet files. One could go a step further and also think about supporting hive-like partitioning.
from crate.
Can you give some more context on concrete use cases?
COPY FROM
is currently designed to do concurrent per shard exports. If you're going to use the parquet files with other tools, I'm not sure if having 1 file per shard is appropriate?
from crate.
Related Issues (20)
- UDFs with parameters incorrectly looked up with names all lowercase when used in GENERATED column HOT 1
- ClassCastException on selecting join field from table with objects HOT 2
- Unexpected results when using `OBJECT` nulls HOT 1
- Add CLI tool to recover a corrupted shard HOT 1
- Inconsistent type mapping when trying to insert scalar and array value for sub-column in `VALUES` statement. HOT 1
- Unexpected results when matching empty string with indexing disabled HOT 2
- Unexpected result when querying partitioned table with null values HOT 2
- Filtering by `= ALL` on float types does not work HOT 2
- Performance of returning a large number of rows HOT 9
- Filtering by `LIKE ANY` with array of regexes does not work HOT 5
- Support for reading and writing using DuckDBs postgres extension HOT 4
- Cluster-Level JWT Configuration HOT 2
- Benchmarks for per-column stored values HOT 5
- Unexpected result when querying partitioned table with `CASE WHEN`
- Unexpected result when using bigint in `ORDER BY` HOT 2
- CrateDB 4.2bis: HTTP bulk operations and error handling with INSERT statements HOT 10
- Only allow RHS of `LIKE ANY` to be patterns HOT 2
- Conflicting column definitions accepted in OBJECT(DYNAMIC) HOT 1
- Casting to NUMERIC from TEXT returns wrong results HOT 3
- Show error messages in result array for failed bulk inserts via HTTP endpoint HOT 1
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from crate.