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Changelog

0.12.0 / 2016-11-03

DataFrame

Array

Bag

  • Fix issue with callables in bag.from_sequence being interpreted as tasks (:pr:`1491`)
  • Avoid non-lazy memory use in reductions (:pr:`1747`)

Administration

  • Added changelog (:pr:`1526`)
  • Create new threadpool when operating from thread (:pr:`1487`)
  • Unify example documentation pages into one (:pr:`1520`)
  • Add versioneer for git-commit based versions (:pr:`1569`)
  • Pass through node_attr and edge_attr keywords in dot visualization (:pr:`1614`)
  • Add continuous testing for Windows with Appveyor (:pr:`1648`)
  • Remove use of multiprocessing.Manager (:pr:`1653`)
  • Add global optimizations keyword to compute (:pr:`1675`)
  • Micro-optimize get_dependencies (:pr:`1722`)

0.11.0 / 2016-08-24

Major Points

DataFrames now enforce knowing full metadata (columns, dtypes) everywhere. Previously we would operate in an ambiguous state when functions lost dtype information (such as apply). Now all dataframes always know their dtypes and raise errors asking for information if they are unable to infer (which they usually can). Some internal attributes like _pd and _pd_nonempty have been moved.

The internals of the distributed scheduler have been refactored to transition tasks between explicit states. This improves resilience, reasoning about scheduling, plugin operation, and logging. It also makes the scheduler code easier to understand for newcomers.

Breaking Changes

  • The distributed.s3 and distributed.hdfs namespaces are gone. Use protocols in normal methods like read_text('s3://...' instead.
  • Dask.array.reshape now errs in some cases where previously it would have create a very large number of tasks

0.10.2 / 2016-07-27

  • More Dataframe shuffles now work in distributed settings, ranging from setting-index to hash joins, to sorted joins and groupbys.
  • Dask passes the full test suite when run when under in Python's optimized-OO mode.
  • On-disk shuffles were found to produce wrong results in some highly-concurrent situations, especially on Windows. This has been resolved by a fix to the partd library.
  • Fixed a growth of open file descriptors that occurred under large data communications
  • Support ports in the --bokeh-whitelist option ot dask-scheduler to better routing of web interface messages behind non-trivial network settings
  • Some improvements to resilience to worker failure (though other known failures persist)
  • You can now start an IPython kernel on any worker for improved debugging and analysis
  • Improvements to dask.dataframe.read_hdf, especially when reading from multiple files and docs

0.10.0 / 2016-06-13

Major Changes

  • This version drops support for Python 2.6
  • Conda packages are built and served from conda-forge
  • The dask.distributed executables have been renamed from dfoo to dask-foo. For example dscheduler is renamed to dask-scheduler
  • Both Bag and DataFrame include a preliminary distributed shuffle.

Bag

  • Add task-based shuffle for distributed groupbys
  • Add accumulate for cumulative reductions

DataFrame

  • Add a task-based shuffle suitable for distributed joins, groupby-applys, and set_index operations. The single-machine shuffle remains untouched (and much more efficient.)
  • Add support for new Pandas rolling API with improved communication performance on distributed systems.
  • Add groupby.std/var
  • Pass through S3/HDFS storage options in read_csv
  • Improve categorical partitioning
  • Add eval, info, isnull, notnull for dataframes

Distributed

  • Rename executables like dscheduler to dask-scheduler
  • Improve scheduler performance in the many-fast-tasks case (important for shuffling)
  • Improve work stealing to be aware of expected function run-times and data sizes. The drastically increases the breadth of algorithms that can be efficiently run on the distributed scheduler without significant user expertise.
  • Support maximum buffer sizes in streaming queues
  • Improve Windows support when using the Bokeh diagnostic web interface
  • Support compression of very-large-bytestrings in protocol
  • Support clean cancellation of submitted futures in Joblib interface

Other

  • All dask-related projects (dask, distributed, s3fs, hdfs, partd) are now building conda packages on conda-forge.
  • Change credential handling in s3fs to only pass around delegated credentials if explicitly given secret/key. The default now is to rely on managed environments. This can be changed back by explicitly providing a keyword argument. Anonymous mode must be explicitly declared if desired.

0.9.0 / 2016-05-11

API Changes

  • dask.do and dask.value have been renamed to dask.delayed
  • dask.bag.from_filenames has been renamed to dask.bag.read_text
  • All S3/HDFS data ingest functions like db.from_s3 or distributed.s3.read_csv have been moved into the plain read_text, read_csv functions, which now support protocols, like dd.read_csv('s3://bucket/keys*.csv')

Array

  • Add support for scipy.LinearOperator
  • Improve optional locking to on-disk data structures
  • Change rechunk to expose the intermediate chunks

Bag

  • Rename from_filename``s to ``read_text
  • Remove from_s3 in favor of read_text('s3://...')

DataFrame

  • Fixed numerical stability issue for correlation and covariance
  • Allow no-hash from_pandas for speedy round-trips to and from-pandas objects
  • Generally reengineered read_csv to be more in line with Pandas behavior
  • Support fast set_index operations for sorted columns

Delayed

  • Rename do/value to delayed
  • Rename to/from_imperative to to/from_delayed

Distributed

  • Move s3 and hdfs functionality into the dask repository
  • Adaptively oversubscribe workers for very fast tasks
  • Improve PyPy support
  • Improve work stealing for unbalanced workers
  • Scatter data efficiently with tree-scatters

Other

  • Add lzma/xz compression support
  • Raise a warning when trying to split unsplittable compression types, like gzip or bz2
  • Improve hashing for single-machine shuffle operations
  • Add new callback method for start state
  • General performance tuning

0.8.1 / 2016-03-11

Array

  • Bugfix for range slicing that could periodically lead to incorrect results.
  • Improved support and resiliency of arg reductions (argmin,

argmax, etc.)

Bag

  • Add zip function

DataFrame

  • Add corr and cov functions
  • Add melt function
  • Bugfixes for io to bcolz and hdf5

0.8.0 / 2016-02-20

Array

  • Changed default array reduction split from 32 to 4
  • Linear algebra, tril, triu, LU, inv, cholesky, solve, solve_triangular, eye``, lstsq, diag, corrcoef.

Bag

  • Add tree reductions
  • Add range function
  • drop from_hdfs function (better functionality now exists in hdfs3 and distributed projects)

DataFrame

  • Refactor dask.dataframe to include a full empty pandas dataframe as metadata. Drop the .columns attribute on Series
  • Add Series categorical accessor, series.nunique, drop the .columns attribute for series.
  • read_csv fixes (multi-column parse_dates, integer column names, etc. )
  • Internal changes to improve graph serialization

Other

  • Documentation updates
  • Add from_imperative and to_imperative functions for all collections
  • Aesthetic changes to profiler plots
  • Moved the dask project to a new dask organization

0.7.6 / 2016-01-05

Array

  • Improve thread safety
  • Tree reductions
  • Add view, compress, hstack, dstack, vstack methods
  • map_blocks can now remove and add dimensions

DataFrame

  • Improve thread safety
  • Extend sampling to include replacement options

Imperative

  • Removed optimization passes that fused results.

Core

  • Removed dask.distributed
  • Improved performance of blocked file reading
  • Serialization improvements
  • Test Python 3.5

0.7.4 / 2015-10-23

This was mostly a bugfix release. Some notable changes:

  • Fix minor bugs associated with the release of numpy 1.10 and pandas 0.17
  • Fixed a bug with random number generation that would cause repeated blocks due to the birthday paradox
  • Use locks in dask.dataframe.read_hdf by default to avoid concurrency issues
  • Change dask.get to point to dask.async.get_sync by default
  • Allow visualization functions to accept general graphviz graph options like rankdir='LR'
  • Add reshape and ravel to dask.array
  • Support the creation of dask.arrays from dask.imperative objects

Deprecation

This release also includes a deprecation warning for dask.distributed, which will be removed in the next version.

Future development in distributed computing for dask is happening here: https://distributed.readthedocs.io . General feedback on that project is most welcome from this community.

0.7.3 / 2015-09-25

Diagnostics

  • A utility for profiling memory and cpu usage has been added to the dask.diagnostics module.

DataFrame

This release improves coverage of the pandas API. Among other things it includes nunique, nlargest, quantile. Fixes encoding issues with reading non-ascii csv files. Performance improvements and bug fixes with resample. More flexible read_hdf with globbing. And many more. Various bug fixes in dask.imperative and dask.bag.

0.7.0 / 2015-08-15

DataFrame

This release includes significant bugfixes and alignment with the Pandas API. This has resulted both from use and from recent involvement by Pandas core developers.

  • New operations: query, rolling operations, drop
  • Improved operations: quantiles, arithmetic on full dataframes, dropna, constructor logic, merge/join, elemwise operations, groupby aggregations

Bag

  • Fixed a bug in fold where with a null default argument

Array

  • New operations: da.fft module, da.image.imread

Infrastructure

  • The array and dataframe collections create graphs with deterministic keys. These tend to be longer (hash strings) but should be consistent between computations. This will be useful for caching in the future.
  • All collections (Array, Bag, DataFrame) inherit from common subclass

0.6.1 / 2015-07-23

Distributed

  • Improved (though not yet sufficient) resiliency for dask.distributed when workers die

DataFrame

  • Improved writing to various formats, including to_hdf, to_castra, and to_csv
  • Improved creation of dask DataFrames from dask Arrays and Bags
  • Improved support for categoricals and various other methods

Array

  • Various bug fixes
  • Histogram function

Scheduling

  • Added tie-breaking ordering of tasks within parallel workloads to better handle and clear intermediate results

Other

  • Added the dask.do function for explicit construction of graphs with normal python code
  • Traded pydot for graphviz library for graph printing to support Python3
  • There is also a gitter chat room and a stackoverflow tag