Changelog for Release 0.8.0-alpha (02/18/2016)

A completely re-architected version of DeepDive is here. Now the system compiles an execution plan ahead of time, checkpoints at a much finer granularity, and gives users full visibility and control of the execution, so any parts of the computation can be flexibly repeated, resumed, or optimized later. The new architecture naturally enforces modularity and extensibility, which enables us to innovate most parts independently without having to understand every possible combination of the entire code. The abstraction layers that encapsulate database operations as well as compute resources are now clearly established, giving a stable ground for extensions in the future that support more types of database engines and compute clusters such as Hadoop/YARN and ones with traditional job schedulers.

As an artifact of this redesign, exciting performance improvements are now observed:

• The database drivers show more than 20x higher throughput (2MB/s -> 50MB/s, per connection) with zero storage footprint by streaming data in and out of UDFs.
• The grounded factor graphs save up to 100x storage space (12GB -> 180MB) by employing compression during the factor graph's grounding and loading, incurring less than 10% overhead in time (400s -> 460s, measuring only the dumping and loading, hence a much smaller fraction in practice).

See the issues and pull requests for this milestone on GitHub (most notably #445) for further details.

New commands and features

An array of new commands have been added to deepdive, and existing ones have been rewritten, such as deepdive initdb and deepdive run.

• Compilation/Execution

  • deepdive compile
  • deepdive plan
  • deepdive do
  • deepdive redo
  • deepdive mark
  • deepdive done

• Learning/Inference

  • deepdive model

• Data management

  • deepdive create
  • deepdive load
  • deepdive unload
  • deepdive query
  • deepdive db

• UDFs

  • deepdive check
  • deepdive compute
  • @tsv_extractor, @returns Python decorators for parsing and formatting in UDFs.

• Interactive tools

  The bundled Mindbender can now automatically construct a search and browsing interface from DDlog annotations. Documentation for Dashboard has been added.

  • mindbender search
  • mindbender dashboard
  • mindbender snapshot
  • mindbender tagger

• Miscellaneous

  • deepdive whereis

To learn more about individual deepdive COMMAND, use the following deepdive help command.

deepdive help COMMAND

Dropped and deprecated features

Scala code base has been completely dropped and rewritten in Bash and jq. Many superfluous features have been dropped and are deprecated to be dropped as summarized below:

• All other extractor style than tsv_extractor, sql_extractor, and cmd_extractor have been dropped, namely:

  • plpy_extractor
  • piggy_extractor
  • json_extractor

• Manually writing deepdive.conf is strongly discouraged as filling in more fields such as dependencies: and input_relations: became mandatory. Rewriting them in DDlog is strongly recommended.

• Database configuration in deepdive.db.default is completely ignored. db.url must be used instead.

• deepdive.extraction.extractors.*.input in deepdive.conf should always be SQL queries. TSV(filename.tsv) or CSV(filename.csv) no longer supported.