* correct reference for `having count(*)`
It turns out that `having count(*) ...` was always treating
the count(*) as exactly 1. After studying this a lot, I noticed
that in fact, we correctly handle other counts. The reason is
that there's already code to recognize aggregates in `having`
clauses as matching aggregates that are being computed -- but
it only covers the other aggregate clause types, not the newly
added `countStarPlanExpression` from making `count(*)` work even
if there's no `_id` field.
We add several corresponding test cases.
* fix sum(a_decimal) type conversion
Added a test case for this, and also added a fix for it.
Underlying issue: qualifiedRefPlanExpression could end up
producing an int64 instead of a pql.Decimal, even though it
had expected type Decimal.
Originally this worked by politely converting an int64 to
a pql.Decimal in the Evaluate phase, but this was not ideal;
the real question is why it was coming out as an int64 at
that step. Showed this to Pat, who spent a while studying it
and produced a better fix.
* temporarily comment out test which fails in DAX
As of September 7, 2022, the Pilosa project is now FeatureBase. The core of the project remains the same: FeatureBase is the first real-time distributed database built entirely on bitmaps. (More information about updated capabilities and improvements below.)
FeatureBase delivers low-latency query results, regardless of throughput or query volumes, on fresh data with extreme efficiency. It works because bitmaps are faster, simpler, and far more I/O efficient than traditional column-oriented data formats. With FeatureBase, you can ingest data from batch data sources (e.g. S3, CSV, Snowflake, BigQuery, etc.) and/or streaming data sources (e.g. Kafka/Confluent, Kinesis, Pulsar).
Install go. Ensure that your shell's search path includes the go/bin directory.
Clone the FeatureBase repository (or download as zip).
In the featurebase directory, run make install to compile the FeatureBase server binary. By default, it will be installed in the go/bin directory.
In the idk directory, run make install to compile the ingester binaries. By default, they will be installed in the go/bin directory.
Run featurebase server --handler.allowed-origins=http://localhost:3000 to run FeatureBase server with default settings (learn more about configuring FeatureBase at the link below). The --handler.allowed-origins parameter allows the standalone web UI to talk to the server; this can be omitted if the web UI is not needed.
Run curl localhost:10101/status to verify the server is running and accessible.
This will ingest the example.csv file into a FeatureBase table called repository. If the table does not exist, it will be automatically created. Learn more about ingesting data into FeatureBase
Query your data.
curl localhost:10101/index/repository/query \
-X POST \
-d 'Row(example=5)'
A lot has changed since the days of Pilosa. This list highlights some new capabilites included in FeatureBase. We have also made signficant improvements to the performance, scalability, and stability of the FeatureBase product.
Query Languages: FeatureBase supports Pilosa Query Language (PQL), as well as SQL
Stream and Batch Ingest: Combine real-time data streams with batch historical data and act on it within milliseconds.
Mutable: Perform inserts, updates, and deletes at scale, in real time and on-the-fly. This is key for meeting data compliance requirements, and for reflecting the constantly-changing nature of high-volume data.
Multi-Valued Set Fields: Store multiple comma-delimited values within a single field while increasing query performance of counts, TopKs, etc.
Time Quantums: Setting a time quantum on a field creates extra views which allow ranged Row queries down to the time interval specified. For example, if the time quantum is set to YMD, ranged Row queries down to the granularity of a day are supported.
RBF storage backend: this is a new compressed bitmap format which improves performance in a number of ways: ACID support on a per shard basis, prevents issues with the number of open files, reduces memory allocation and lock contention for reads, provides more consistent garbage collection, and allows backups to run concurrently with writes. However, because of this change, Pilosa backup files cannot be restored into FeatureBase.
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