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There's a lot going on here. First, we were treating "the test is a Condition" as implying BSI, which it doesn't anymore. Second, the behavior of conditions was weird and BSI-specific. Third, we had to propagate these changes and features throughout a bunch of code, including both the core featurebase code and the DAX replacements/copies of it, plus the SQL3 layer. We refactor this so that tests for equality and inequality work for non-BSI fields, so now if you accidentally use `==` in a Row call on a non-BSI field, it still works; that's not specific to BSI fields anymore. We add a TrackExistence flag to fields, and propagate it through things like our protobuf code, etcetera, so that we can successfully create fields. Newly-created fields get this by default, because we add it unconditionally to them, but the paths that are being called with existing fields don't add it. So, when we "create" (really, just load the definition of) a field from something stored in the schema, we don't add TrackExistence to it, but any path to creating a new field should. A time quantum field with NoStandardView will *effectively* lack TrackExistence. For sets, mutexes, and time quantums with a standard view, anything that sets bits will also set a corresponding bit for the record in a new "existence" view. This allows us to distinguish between an empty set and a null, and also allows null checks to be constant-time. When clearing bits, we don't clear existence bits EXCEPT that if you clear a bit in a mutex, *and the bit actually existed*, we clear the existence bit. For sets and time quantums, clearing bits never clears the existence bit. Deleting records clears the existence bit. We also add code to the `batch` subpackage to generate suitable existence field bitmaps and import them. This logic correctly handles empty sets and nils. The `batch` package does not allow specification of anything equivalent to clearing a single bit from an existing record, so we don't have to deal with the mutex complexity in that case, which is good because it would be impossible. This requires a number of other subtle changes, such as allowing new fields to have more than one FieldOption specified for them. We also drop the handful of implementation bits relating to the "fullySorted" internal-use-only import flag, which existed only to support the JSON ingest API, which we've removed. The most dangerous part of this is that the mutex semantics are impossible to implement on top of our existing API, because they require us to know, not how *many* bits we cleared, but which *specific* bits we cleared. I've implemented this as a new Tx method, which is almost certainly going to be tech debt one day; if we some day drop the Import API, we should remove that. The testing for this is only currently covering the Set/Clear behavior of PQL, and the Import API. The batch tests haven't been written yet. Fields that don't have existence tracking enabled refuse to perform null/not-null tests. They should also report themselves as having no null values -- if a record exists, sets in it are considered empty rather than null. The SQL3 support requires a number of subtle modifications to both featurebase and some addon tooling. The essential thing is dropping the unconditional translation of nil slices to non-nil empty slices in translateResult, both in the executor and the orchestrator. We also modify the logic that handles generating results from Extract calls, to ensure that non-null sets get an empty slice created for them even if they never have any values assigned. The expected results for some tests are different now; we expect to get nil slices, rather than 0-length non-nil slices, for fields which were never written for a given record. Most tests were not changed. (In every case, if a test was failing, I actually checked the logic before changing expected results. This required a lot of tracking down of edge cases.) The batch package now rejects as an error attempts to clear single bits from mutex fields, because so far as I can tell it's simply impossible to have a roaring import that specifies the correct semantics there; you can't tell whether to clear an existence bit without access to the currently-set bits, which the batch API doesn't have. We already supported the special case of specifying a clear value of nil for clearing a mutex field; now that is the only allowed value for a mutex field to have in row.Clears. We change the logic for fixing up incoming view names (in two places) to stop assuming that any view in a time field other than "" that does not have viewStandard as a prefix is a partial time quantum name that should have "standard_" prepended to it. This allows us to submit bitmaps for "existence" to time quantum fields and not have them silently transformed into "standard_existence" because that's what we'd do with "202203". We drop the field ClearBits method, which was totally unused. We drop the sliceDifference function, which was used in a previous mutex implementation and hasn't been used in ages, and the test case for it, and the helper function used only by that test case. |
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FeatureBase
Pilosa is now FeatureBase
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).
For more information about FeatureBase, please visit www.featurebase.com.
Getting Started
Build FeatureBase Server from source
- 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 installto compile the FeatureBase server binary. By default, it will be installed in the go/bin directory. - In the idk directory, run
make installto compile the ingester binaries. By default, they will be installed in the go/bin directory. - Run
featurebase server --handler.allowed-origins=http://localhost:3000to run FeatureBase server with default settings (learn more about configuring FeatureBase at the link below). The--handler.allowed-originsparameter 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/statusto verify the server is running and accessible.
Ingest Data and Query
- Run
molecula-consumer-csv \
--index repository \
--header "language__ID_F,project_id__ID_F" \
--id-field project_id \
--batch-size 1000 \
--files example.csv
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)'
Learn about supported SQL, native Pilosa Query Language (PQL).
Data Model
Because FeatureBase is built on bitmaps, there is bit of a learning curve to grasp how your data is represented. Learn about Data Modeling.
More Information
Community
You can email us at community@featurebase.com or learn more about contributing at https://www.featurebase.com/community.
Chat with us: https://discord.gg/FBn2vEp7Na
What's Changed Since the Pilosa Days?
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.
License
FeatureBase is licensed under the Apache License, Version 2.0