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Proposed changes
Doris's Routine Load realizes the import of data from Kafka to Doris, and make a feature of real-time synchronization of data from Pulsar to Doris based on Pulsar IO.
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The custom abstract class DorisAbstractSink implements the interface org.apache.pulsar.io.core.Sink.
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The DorisGenericRecordSink class inherits the abstract class DorisAbstractSink, and the speci
Use case:
Add the ability to search for an element in an array and retrieve back the array index.
As per the PostgreSQL documentation:
array_position ( anycompatiblearray, anycompatible [, integer ] ) → integer
Returns the subscript of the first occurrence of the second argument in the array, or NULL if it's not present
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Hey everyone!
mapd-core-cpu is already available on conda-forge (https://anaconda.org/conda-forge/omniscidb-cpu)
now we should add some instructions on the documentation.
at this moment it is available for linux and osx.
some additional information about the configuration:
- for now, always install
omniscidb-cpuinside a conda environment (also it is a good practice), eg:
bitmap_max: input: Bitmap output: Bigint
return the max value of bitmap, if input was null return null
- We need to implement the max/min functions in the class BitmapValue in be/src/util/bitmap_value.h
- define bitmap_max and bitmap_min in be/src/exprs/vectorized/bitmap_functions.h
- implement bitmap_min and bitmap_max in bitmap_function.cpp, you can refer to bitmap_count for the impleme
Describe the bug
If the column names in a CSV file are uppercase, then typing lowercase column names in SQL queries will result in an Error: Invalid identifier for schema.
To Reproduce
Here is a simple example program that runs a query on 2 tables.
#[tokio::main]
async fn main() -> datafusion::error::Result<()> {
let mut ctx = ExecutionContext::new();
ctx.regis-
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There seems to be significant API changes in bitvec. The SIMD code seems to suffer most and need some rewrite.
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AOE v0.1.0 limitations:
- AOE has a global hard limit of ? table count.
- AOE has a hard limit of ? columns per table.
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In Big Query there is a function array_concat_agg that aggregates array fields by concatenating the arrays. In Snowflake there is a flatten function that can unnest nested arrays into single array. I am looking for similar functionality in duckdb.
select flatten([[1, 2], [2, 3], [4, 5]]would return[1, 2, 2, 3, 4, 5]I would also need a distinct option:
`select flatten(DISTINCT [[1, 2],