Note: We now skip a test because we can't pass it but fixing
it is presently beyond my understanding. In parser_test.go,
we skip
SELECT * FROM X INNER JOIN Y ON true INNER JOIN Z ON false
because if we stringify it, we put the "ON true" in the wrong
place, and end up with something we can't parse.
The main change here is modifying AssertParseStatement and
AssertStatementStringer (and the corresponding Expression
functions) to also verify that they can clone and round-trip,
and that we can walk expressions. This gives us a ton
more coverage of Clone and conversions to string, and caught
a number of subtle typos and missing type switch cases.
Some of the changes are mostly cosmetic, such as only
including optional words when stringifying expressions
or statements if those optional words were present originally,
as shown by the Pos value stored for those words.
We also drop a lot of trailing apostrophes from some of the
parse test cases, which appear to be harmless but won't be
reproduced when converting back to strings.
We also add a number of additional test cases, or add
clauses to existing test cases, to improve coverage of a
lot of error testing. For instance, we added a decimal
field to the tests of show table, and added cases
using KEYPARTITIONS. (Although it doesn't *do* anything.)
Similarly, whenever we create a statement, we check
the behavior of requesting a list of sources from it, to
verify that source finding code at least runs.
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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