* Refactor CLI to mimic psql's meta-commands
This PR adds support for meta-commands (also known as "backslash
commands") like those in psql, Postgres's CLI. Only a few meta-commands
are currently implemented, but this was meant to demonstrate how we
could use something like `\i file.csv` to insert local files into SQL
statements.
* Meta-commands: \file and \include
The initial implementation used `\i` as a streaming file handle.
This commit changes that to `\file`, and then implements `\i` (or
`\include`) as handling multiple sql commands.
* Add meta-command "help" (\?)
This is basically a copy of the psql help output, but includes only
those options we currently support.
* Add support for \o [file], and \timing
The \o meta-command writes query output to a file.
The \timing meta-command turns on/off the timing display sent to stdout.
* Add meta-commands: \l (show databases) and \dt (show tables)
* Add meta-command: \watch [period]
* Update meta-command \connect to take database name instead of ID
* Add support for \echo, \qecho, and \warn
This commit contains an known issue in that the `-n` option will exclude
the line feed, but if the output is the terminal, the readline package
clobbers any content on the current line (i.e. anything without a line
feed). That will need to be addressed at some point.
* Add support for \w [FILE] (write query buffer to file)
* Add SchemaAPI no-op implementation
* Refactor query handler to align with /sql and /databases endpoints
We want to standardize on:
/sql
/databases/{databaseID}/sql
* Add CLI support for expanded, border, tuples_only (and pset)
* Add help text for \pset and \t
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.