With AresDB, strings are converted to enumerated types enums automatically before they enter the database for better storage and query efficiency. This allows case-sensitive equality checking, but does not support advanced operations such as concatenation, substrings, globs, and regex matching.
We intend to add full string support in the future. AresDB stores all data in a columnar format. The values of each column are stored as a columnar value vector. AresDB stores uncompressed and unsorted columnar data live vectors in a live store. Data records in a live store are partitioned into live batches of configured capacity. New batches are created at ingestion, while old batches are purged after records are archived.
A primary key index is used to locate the records for deduplication and updates. Figure 3, below, demonstrates how we organize live records and use a primary key value to locate them:. The values of each column within a batch are stored as a columnar vector.
AresDB also stores mature, sorted, and compressed columnar data archive vectors in an archive store via fact tables. Records in archive store are also partitioned into batches. Archive batch uses the number of days since Unix Epoch as its batch ID. Records are kept sorted according to a user configured column sort order.
The goal of configuring the user-configured column sort order is to:. A column is compressed only if it appears in the user-configured sort order.
We do not attempt to compress high cardinality columns because the amount of storage saved by compressing high cardinality columns is negligible. After sorting, the data for each qualified column is compressed using a variation of run-length encoding. In addition to the value vector and null vector, we introduce the count vector to represent a repetition of the same value.
The upsert batch is a custom, serialized binary format that minimizes space overhead while still keeping the data randomly accessible. When AresDB receives an upsert batch for ingestion, it first writes the upsert batch to redo logs for recovery.
After an upsert batch is appended to the end of the redo log, AresDB then identifies and skips late records on fact tables for ingestion into the live store. As depicted in Figure 6, below, brand new records not seen before based on the primary key value will be applied to the empty space while existing records will be updated directly:. We periodically run a scheduled process, referred to as archiving, on live store records to merge the new records records that have never been archived before into the archive store.
Archiving will only process records in the live store with their event time falling into the range of the old cut-off time the cut-off time from last archiving process and new cut-off time the new cut-off time based on the archive delay setting in the table schema. Archiving does not require primary key value index deduplication during merging since only records between the old cut-off and new cut-off ranges will be archived.
In this scenario, the archiving interval is the time between two archiving runs, while the archiving delay is the duration after the event time but before an event can be archived. As shown in Figure 7, above, old records with event time older than the archiving cut-off for fact tables are appended to the backfill queue and eventually handled by the backfill process. This process is also triggered by the time or size of the backfill queue onces it reaches its threshold. Compared to ingestion by the live store, backfilling is asynchronous and relatively more expensive in terms of CPU and memory resources.
Backfill is used in the following scenarios:. Unlike archiving, backfilling is idempotent and requires primary key value-based deduplication.
The data being backfilled will eventually be visible to queries. The backfill queue is maintained in memory with a pre-configured size, and, during massive backfill loads, the client will be blocked from proceeding before the queue is cleared by a backfill run.
In JSON-format, AQL provides better programmatic query experience than SQL for dashboard and decision system developers, because it allows them to easily compose and manipulate queries using code without worrying about issues like SQL injection.
It serves as the universal query format on typical architectures from web browsers, front-end servers, and back-end servers, all the way back into the database AresDB. In addition, AQL provides handy syntactic sugar for time filtering and bucketization, with native time zone support. The language also supports features like implicit sub-queries to avoid common query mistakes and makes query analysis and rewriting easy for back-end developers.
Exposing a SQL interface for querying is one of the next steps that we will look into to enhance the AresDB user experience. We depict the AQL query execution flow in Figure 8, below:. An AQL query is compiled into internal query context. Expressions in filters, dimensions, and measurements are parsed into abstract syntax trees AST for later processing via GPU.
AresDB utilizes pre-filters to cheaply filter archived data before sending them to a GPU for parallel processing. Since archived data is sorted according to a configured column order, some filters may be able to utilize this sorted order by applying binary search to locate the corresponding matching range.
After prefiltering, only the green values satisfying filter condition need to be pushed to the GPU for parallel processing. Input data is fed to the GPU and executed there one batch at a time. This includes both live batches and archive batches. Two streams are used alternately on each query for processing in two overlapping stages.
In Figure 10, below, we offer a timeline illustration of this process:. Sort options. Star Updated Nov 13, Jupyter Notebook. Updated Apr 29, Scala. Predict NYC taxi travel times Kaggle competition. Updated Nov 3, Jupyter Notebook. Star 7. Updated Mar 9, Jupyter Notebook. Star 6. Updated Mar 22, JavaScript.
Star 5. Updated Jun 30, Star 4. Updated Sep 21, Jupyter Notebook. Star 3. DevCon5 Community Meet up. Updated Sep 30, Jupyter Notebook. Updated Jan 15, Python. Star 2. Updated Apr 2, R. Updated Jun 13, Jupyter Notebook. It is used to conduct time series inferences and forecasting with structural Bayesian time series models for real-world cases and research. Like many other ML use cases, the best model structure is largely dependent on that particular use case and available data. A common approach to determine a well-suited model is to try out different model types, perform backtesting, and diagnose how well they perform.
Having one, consistent interface for performing all these tasks largely simplifies our ability to determine the best model for a given use case. While there are plenty of time series model implementations in the Python ecosystem, Orbit aims to provide a consistent Python interface to simplify Bayesian time-series modeling workflow by linking one command to each step in the following diagram.
Check out the How to Use Orbit? We follow a number of object-oriented design patterns to maintain independence between Estimators and Models. What that means for the end users is that there will be consistency in package usage to accomplish a desired use case.
What that means for model developers is that their focus can be on the structure of the model, rather than the Orbit interface or interactions with the underlying PPL. It may seem unnecessary to implement this class if we are using an underlying PPL, however, in addition to the nuances of each PPL, a user must also determine:.
We abstract away some of the decisions, and allow a Model to compose a specified Estimator within it. A Model is a concrete implementation of a model structure, and how to perform inference after the parameter estimation. We found that for our use cases, the current two model implementations performed well relative to other solutions. One important goal of Orbit is to quickly incorporate new models while maintaining a consistent interface.
Furthermore, we have modules for backtesting, diagnostics, and visualization that will work out of box with any Orbit model object. To be specific, our package provides:. For more details, read our quick start guide and documentation page. After model training, the predictions can be visualized at convenience. Orbit also provides a rich set of plotting tools to visualize and diagnose the posterior distribution of sampling parameters and the trace of posteriors.
The Orbit package also comes with a general-purpose backtesting utility, to evaluate the time series forecasting performance.
Two backtesting schemes are supported: expanding window and rolling window. Figure 2 presented earlier is an animation to visualize the forecasting results by using the expanding window scheme with 6 splits. Currently, we implemented two major types of Bayesian structural time series models in Orbit:.
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