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Posted to commits@beam.apache.org by da...@apache.org on 2016/11/05 17:12:53 UTC
[50/53] [partial] incubator-beam-site git commit: Move files
http://git-wip-us.apache.org/repos/asf/incubator-beam-site/blob/cc22c462/_beam_team/team.md
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diff --git a/_beam_team/team.md b/_beam_team/team.md
deleted file mode 100644
index 9368c37..0000000
--- a/_beam_team/team.md
+++ /dev/null
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----
-group: "Beam Committers"
-description: "The following is a list of developers with commit privileges that have directly contributed to the project in one way or another."
-members:
- - name: Aljoscha Krettek
- apache_id: aljoscha
- email: aljoscha [at] apache [dot] org
- organization: data Artisans
- roles: committer, PPMC
- time_zone: "+1"
- - name: Amit Sela
- apache_id: amitsela
- email: amitsela [at] apache [dot] org
- organization: PayPal
- roles: committer, PPMC
- time_zone: "+2"
- - name: Ben Chambers
- apache_id: bchambers
- email: bchambers [at] apache [dot] org
- organization: Google
- roles: committer, PPMC
- time_zone: "-8"
- - name: Craig Chambers
- apache_id:
- email:
- organization: Google
- roles: committer, PPMC
- time_zone: "-8"
- - name: Dan Halperin
- apache_id: dhalperi
- email: dhalperi [at] apache [dot] org
- organization: Google
- roles: committer, PPMC
- time_zone: "-8"
- - name: Davor Bonaci
- apache_id: davor
- email: davor [at] apache [dot] org
- organization: Google
- roles: committer, PPMC
- time_zone: "-8"
- - name: Frances Perry
- apache_id: frances
- email: frances [at] apache [dot] org
- organization: Google
- roles: committer, PPMC
- time_zone: "-8"
- - name: James Malone
- apache_id: jamesmalone
- email: jamesmalone [at] apache [dot] org
- organization: Google
- roles: committer, PPMC
- time_zone: "-8"
- - name: Jean-Baptiste Onofr�
- apache_id: jbonofre
- email: jbonofre [at] apache [dot] org
- organization: Talend
- roles: champion, committer, PPMC
- time_zone: "+1"
- - name: Jesse Anderson
- apache_id:
- email: jesse [at] smokinghand [dot] com
- organization: Smoking Hand
- roles: committer
- time_zone: "-8"
- - name: Josh Wills
- apache_id: jwills
- email: jwills [at] apache [dot] org
- organization:
- roles: committer, PPMC
- time_zone: "-8"
- - name: Kostas Tzoumas
- apache_id: ktzoumas
- email: kostas [at] data-artisans [dot] com
- organization: data Artisans
- roles: committer, PPMC
- time_zone: "+1"
- - name: Kenneth Knowles
- apache_id: kenn
- email: kenn [at] apache [dot] org
- organization: Google
- roles: committer, PPMC
- time_zone: "-8"
- - name: Luke Cwik
- apache_id: lcwik
- email: lcwik [at] apache [dot] org
- organization: Google
- roles: committer, PPMC
- time_zone: "-8"
- - name: Maximilian Michels
- apache_id: mxm
- email: mxm [at] apache [dot] org
- organization: data Artisans
- roles: committer, PPMC
- time_zone: "+1"
- - name: Stephan Ewen
- apache_id: sewen
- email: stephan [at] data-artisans [dot] com
- organization: data Artisans
- roles: committer, PPMC
- time_zone: "+1"
- - name: Thomas Groh
- apache_id:
- email:
- organization: Google
- roles: committer
- time_zone: "-8"
- - name: Thomas Weise
- apache_id: thw
- email: thw [at] apache [dot] org
- organization: DataTorrent
- roles: committer
- time_zone: "-8"
- - name: Tom White
- apache_id: tomwhite
- email: tom [at] cloudera [dot] com
- organization: Cloudera
- roles: committer, PPMC
- time_zone: "-8"
- - name: Tyler Akidau
- apache_id: takidau
- email: takidau [at] apache [dot] org
- organization: Google
- roles: committer, PPMC
- time_zone: "-8"
- - name: Robert Bradshaw
- apache_id: robertwb
- email: robertwb [at] apache [dot] org
- organization: Google
- roles: committer, PPMC
- time_zone: "-8"
----
http://git-wip-us.apache.org/repos/asf/incubator-beam-site/blob/cc22c462/_data/authors.yml
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diff --git a/_data/authors.yml b/_data/authors.yml
deleted file mode 100644
index ffcb419..0000000
--- a/_data/authors.yml
+++ /dev/null
@@ -1,34 +0,0 @@
-aljoscha:
- name: Aljoscha Krettek
- email: aljoscha@apache.org
- twitter: aljoscha
-davor:
- name: Davor Bonaci
- email: davor@apache.org
- twitter: BonaciDavor
-dhalperi:
- name: Dan Halperin
- email: dhalperi@apache.org
- twitter:
-fjp:
- name: Frances Perry
- email: frances@apache.org
- twitter: francesjperry
-jamesmalone:
- name: James Malone
- email: jamesmalone@apache.org
- twitter: chimerasaurus
-robertwb:
- name: Robert Bradshaw
- email: robertwb@apache.org
- twitter:
-takidau:
- name: Tyler Akidau
- email: takidau@apache.org
- twitter: takidau
-tgroh:
- name: Thomas Groh
- email: tgroh@google.com
-jesseanderson:
- name: Jesse Anderson
- twitter: jessetanderson
http://git-wip-us.apache.org/repos/asf/incubator-beam-site/blob/cc22c462/_data/capability-matrix.yml
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diff --git a/_data/capability-matrix.yml b/_data/capability-matrix.yml
deleted file mode 100644
index c61b68b..0000000
--- a/_data/capability-matrix.yml
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@@ -1,559 +0,0 @@
-columns:
- - class: model
- name: Beam Model
- - class: dataflow
- name: Google Cloud Dataflow
- - class: flink
- name: Apache Flink
- - class: spark
- name: Apache Spark
-
-categories:
- - description: What is being computed?
- anchor: what
- color-b: 'ca1'
- color-y: 'ec3'
- color-p: 'fe5'
- color-n: 'ddd'
- rows:
- - name: ParDo
- values:
- - class: model
- l1: 'Yes'
- l2: element-wise processing
- l3: Element-wise transformation parameterized by a chunk of user code. Elements are processed in bundles, with initialization and termination hooks. Bundle size is chosen by the runner and cannot be controlled by user code. ParDo processes a main input PCollection one element at a time, but provides side input access to additional PCollections.
- - class: dataflow
- l1: 'Yes'
- l2: fully supported
- l3: Batch mode uses large bundle sizes. Streaming uses smaller bundle sizes.
- - class: flink
- l1: 'Yes'
- l2: fully supported
- l3: ParDo itself, as per-element transformation with UDFs, is fully supported by Flink for both batch and streaming.
- - class: spark
- l1: 'Yes'
- l2: fully supported
- l3: ParDo applies per-element transformations as Spark FlatMapFunction.
- - name: GroupByKey
- values:
- - class: model
- l1: 'Yes'
- l2: key grouping
- l3: Grouping of key-value pairs per key, window, and pane. (See also other tabs.)
- - class: dataflow
- l1: 'Yes'
- l2: fully supported
- l3: ''
- - class: flink
- l1: 'Yes'
- l2: fully supported
- l3: "Uses Flink's keyBy for key grouping. When grouping by window in streaming (creating the panes) the Flink runner uses the Beam code. This guarantees support for all windowing and triggering mechanisms."
- - class: spark
- l1: 'Partially'
- l2: fully supported in batch mode
- l3: "Using Spark's <tt>groupByKey</tt>. GroupByKey with multiple trigger firings in streaming mode is a work in progress."
- - name: Flatten
- values:
- - class: model
- l1: 'Yes'
- l2: collection concatenation
- l3: Concatenates multiple homogenously typed collections together.
- - class: dataflow
- l1: 'Yes'
- l2: fully supported
- l3: ''
- - class: flink
- l1: 'Yes'
- l2: fully supported
- l3: ''
- - class: spark
- l1: 'Yes'
- l2: fully supported
- l3: ''
-
- - name: Combine
- values:
- - class: model
- l1: 'Yes'
- l2: associative & commutative aggregation
- l3: 'Application of an associative, commutative operation over all values ("globally") or over all values associated with each key ("per key"). Can be implemented using ParDo, but often more efficient implementations exist.'
- - class: dataflow
- l1: 'Yes'
- l2: 'efficient execution'
- l3: ''
- - class: flink
- l1: 'Yes'
- l2: 'fully supported'
- l3: Uses a combiner for pre-aggregation for batch and streaming.
- - class: spark
- l1: 'Yes'
- l2: fully supported
- l3: "Using Spark's <tt>combineByKey</tt> and <tt>aggregate</tt> functions."
-
- - name: Composite Transforms
- values:
- - class: model
- l1: 'Yes'
- l2: user-defined transformation subgraphs
- l3: Allows easy extensibility for library writers. In the near future, we expect there to be more information provided at this level -- customized metadata hooks for monitoring, additional runtime/environment hooks, etc.
- - class: dataflow
- l1: 'Partially'
- l2: supported via inlining
- l3: Currently composite transformations are inlined during execution. The structure is later recreated from the names, but other transform level information (if added to the model) will be lost.
- - class: flink
- l1: 'Partially'
- l2: supported via inlining
- l3: ''
- - class: spark
- l1: 'Partially'
- l2: supported via inlining
- l3: ''
-
- - name: Side Inputs
- values:
- - class: model
- l1: 'Yes'
- l2: additional elements available during DoFn execution
- l3: Side inputs are additional <tt>PCollections</tt> whose contents are computed during pipeline execution and then made accessible to DoFn code. The exact shape of the side input depends both on the <tt>PCollectionView</tt> used to describe the access pattern (interable, map, singleton) and the window of the element from the main input that is currently being processed.
- - class: dataflow
- l1: 'Yes'
- l2: some size restrictions in streaming
- l3: Batch mode supports a distributed implementation, but streaming mode may force some size restrictions. Neither mode is able to push lookups directly up into key-based sources.
- - class: flink
- l1: 'Yes'
- l2: some size restrictions in streaming
- l3: Batch mode supports a distributed implementation, but streaming mode may force some size restrictions. Neither mode is able to push lookups directly up into key-based sources.
- - class: spark
- l1: 'Yes'
- l2: fully supported
- l3: "Using Spark's broadcast variables. In streaming mode, side inputs may update but only between micro-batches."
-
- - name: Source API
- values:
- - class: model
- l1: 'Yes'
- l2: user-defined sources
- l3: Allows users to provide additional input sources. Supports both bounded and unbounded data. Includes hooks necessary to provide efficient parallelization (size estimation, progress information, dynamic splitting, etc).
- - class: dataflow
- l1: 'Yes'
- l2: fully supported
- l3:
- - class: flink
- l1: 'Yes'
- l2: fully supported
- l3:
- - class: spark
- l1: 'Yes'
- l2: fully supported
- l3:
-
- - name: Aggregators
- values:
- - class: model
- l1: 'Partially'
- l2: user-provided metrics
- l3: Allow transforms to aggregate simple metrics across bundles in a <tt>DoFn</tt>. Semantically equivalent to using a side output, but support partial results as the transform executes. Will likely want to augment <tt>Aggregators</tt> to be more useful for processing unbounded data by making them windowed.
- - class: dataflow
- l1: 'Partially'
- l2: may miscount in streaming mode
- l3: Current model is fully supported in batch mode. In streaming mode, <tt>Aggregators</tt> may under or overcount when bundles are retried.
- - class: flink
- l1: 'Partially'
- l2: may undercount in streaming
- l3: Current model is fully supported in batch. In streaming mode, <tt>Aggregators</tt> may undercount.
- - class: spark
- l1: 'Partially'
- l2: may overcount when tasks are retried in transformations.
- l3: 'supported via <tt>AccumulatorParam</tt> mechanism. If a task retries, and the accumulator is not within a Spark "Action", an overcount is possible.'
-
- - name: Keyed State
- values:
- - class: model
- jira: BEAM-25
- l1: 'No'
- l2: storage per key, per window
- l3: Allows fine-grained access to per-key, per-window persistent state. Necessary for certain use cases (e.g. high-volume windows which store large amounts of data, but typically only access small portions of it; complex state machines; etc.) that are not easily or efficiently addressed via <tt>Combine</tt> or <tt>GroupByKey</tt>+<tt>ParDo</tt>.
- - class: dataflow
- l1: 'No'
- l2: pending model support
- l3: Dataflow already supports keyed state internally, so adding support for this should be easy once the Beam model exposes it.
- - class: flink
- l1: 'No'
- l2: pending model support
- l3: Flink already supports keyed state, so adding support for this should be easy once the Beam model exposes it.
- - class: spark
- l1: 'No'
- l2: pending model support
- l3: Spark supports keyed state with mapWithState() so support shuold be straight forward.
-
-
- - description: Where in event time?
- anchor: where
- color-b: '37d'
- color-y: '59f'
- color-p: '8cf'
- color-n: 'ddd'
- rows:
- - name: Global windows
- values:
- - class: model
- l1: 'Yes'
- l2: all time
- l3: The default window which covers all of time. (Basically how traditional batch cases fit in the model.)
- - class: dataflow
- l1: 'Yes'
- l2: default
- l3: ''
- - class: flink
- l1: 'Yes'
- l2: supported
- l3: ''
- - class: spark
- l1: 'Yes'
- l2: supported
- l3: ''
-
- - name: Fixed windows
- values:
- - class: model
- l1: 'Yes'
- l2: periodic, non-overlapping
- l3: Fixed-size, timestamp-based windows. (Hourly, Daily, etc)
- - class: dataflow
- l1: 'Yes'
- l2: built-in
- l3: ''
- - class: flink
- l1: 'Yes'
- l2: supported
- l3: ''
- - class: spark
- l1: 'Yes'
- l2: supported
- l3: ''
-
- - name: Sliding windows
- values:
- - class: model
- l1: 'Yes'
- l2: periodic, overlapping
- l3: Possibly overlapping fixed-size timestamp-based windows (Every minute, use the last ten minutes of data.)
- - class: dataflow
- l1: 'Yes'
- l2: built-in
- l3: ''
- - class: flink
- l1: 'Yes'
- l2: supported
- l3: ''
- - class: spark
- l1: 'Yes'
- l2: supported
- l3: ''
-
- - name: Session windows
- values:
- - class: model
- l1: 'Yes'
- l2: activity-based
- l3: Based on bursts of activity separated by a gap size. Different per key.
- - class: dataflow
- l1: 'Yes'
- l2: built-in
- l3: ''
- - class: flink
- l1: 'Yes'
- l2: supported
- l3: ''
- - class: spark
- l1: 'Yes'
- l2: supported
- l3: ''
-
- - name: Custom windows
- values:
- - class: model
- l1: 'Yes'
- l2: user-defined windows
- l3: All windows must implement <tt>BoundedWindow</tt>, which specifies a max timestamp. Each <tt>WindowFn</tt> assigns elements to an associated window.
- - class: dataflow
- l1: 'Yes'
- l2: supported
- l3: ''
- - class: flink
- l1: 'Yes'
- l2: supported
- l3: ''
- - class: spark
- l1: 'Yes'
- l2: supported
- l3: ''
-
- - name: Custom merging windows
- values:
- - class: model
- l1: 'Yes'
- l2: user-defined merging windows
- l3: A custom <tt>WindowFn</tt> additionally specifies whether and how to merge windows.
- - class: dataflow
- l1: 'Yes'
- l2: supported
- l3: ''
- - class: flink
- l1: 'Yes'
- l2: supported
- l3: ''
- - class: spark
- l1: 'Yes'
- l2: supported
- l3: ''
-
- - name: Timestamp control
- values:
- - class: model
- l1: 'Yes'
- l2: output timestamp for window panes
- l3: For a grouping transform, such as GBK or Combine, an OutputTimeFn specifies (1) how to combine input timestamps within a window and (2) how to merge aggregated timestamps when windows merge.
- - class: dataflow
- l1: 'Yes'
- l2: supported
- l3: ''
- - class: flink
- l1: 'Yes'
- l2: supported
- l3: ''
- - class: spark
- l1: 'Yes'
- l2: supported
- l3: ''
-
-
-
- - description: When in processing time?
- anchor: when
- color-b: '6a4'
- color-y: '8c6'
- color-p: 'ae8'
- color-n: 'ddd'
- rows:
-
- - name: Configurable triggering
- values:
- - class: model
- l1: 'Yes'
- l2: user customizable
- l3: Triggering may be specified by the user (instead of simply driven by hardcoded defaults).
- - class: dataflow
- l1: 'Yes'
- l2: fully supported
- l3: Fully supported in streaming mode. In batch mode, intermediate trigger firings are effectively meaningless.
- - class: flink
- l1: 'Yes'
- l2: fully supported
- l3: ''
- - class: spark
- l1: 'No'
- l2: ''
- l3: ''
-
- - name: Event-time triggers
- values:
- - class: model
- l1: 'Yes'
- l2: relative to event time
- l3: Triggers that fire in response to event-time completeness signals, such as watermarks progressing.
- - class: dataflow
- l1: 'Yes'
- l2: yes in streaming, fixed granularity in batch
- l3: Fully supported in streaming mode. In batch mode, currently watermark progress jumps from the beginning of time to the end of time once the input has been fully consumed, thus no additional triggering granularity is available.
- - class: flink
- l1: 'Yes'
- l2: fully supported
- l3: ''
- - class: spark
- l1: 'No'
- l2: ''
- l3: ''
-
- - name: Processing-time triggers
- values:
- - class: model
- l1: 'Yes'
- l2: relative to processing time
- l3: Triggers that fire in response to processing-time advancing.
- - class: dataflow
- l1: 'Yes'
- l2: yes in streaming, fixed granularity in batch
- l3: Fully supported in streaming mode. In batch mode, from the perspective of triggers, processing time currently jumps from the beginning of time to the end of time once the input has been fully consumed, thus no additional triggering granularity is available.
- - class: flink
- l1: 'Yes'
- l2: fully supported
- l3: ''
- - class: spark
- l1: 'Yes'
- l2: "This is Spark streaming's native model"
- l3: "Spark processes streams in micro-batches. The micro-batch size is actually a pre-set, fixed, time interval. Currently, the runner takes the first window size in the pipeline and sets it's size as the batch interval. Any following window operations will be considered processing time windows and will affect triggering."
-
- - name: Count triggers
- values:
- - class: model
- l1: 'Yes'
- l2: every N elements
- l3: Triggers that fire after seeing at least N elements.
- - class: dataflow
- l1: 'Yes'
- l2: fully supported
- l3: Fully supported in streaming mode. In batch mode, elements are processed in the largest bundles possible, so count-based triggers are effectively meaningless.
- - class: flink
- l1: 'Yes'
- l2: fully supported
- l3: ''
- - class: spark
- l1: 'No'
- l2: ''
- l3: ''
-
- - name: '[Meta]data driven triggers'
- values:
- - class: model
- jira: BEAM-101
- l1: 'No'
- l2: in response to data
- l3: Triggers that fire in response to attributes of the data being processed.
- - class: dataflow
- l1: 'No'
- l2: pending model support
- l3:
- - class: flink
- l1: 'No'
- l2: pending model support
- l3:
- - class: spark
- l1: 'No'
- l2: pending model support
- l3:
-
- - name: Composite triggers
- values:
- - class: model
- l1: 'Yes'
- l2: compositions of one or more sub-triggers
- l3: Triggers which compose other triggers in more complex structures, such as logical AND, logical OR, early/on-time/late, etc.
- - class: dataflow
- l1: 'Yes'
- l2: fully supported
- l3: ''
- - class: flink
- l1: 'Yes'
- l2: fully supported
- l3: ''
- - class: spark
- l1: 'No'
- l2: ''
- l3: ''
-
- - name: Allowed lateness
- values:
- - class: model
- l1: 'Yes'
- l2: event-time bound on window lifetimes
- l3: A way to bound the useful lifetime of a window (in event time), after which any unemitted results may be materialized, the window contents may be garbage collected, and any addtional late data that arrive for the window may be discarded.
- - class: dataflow
- l1: 'Yes'
- l2: fully supported
- l3: Fully supported in streaming mode. In batch mode no data is ever late.
- - class: flink
- l1: 'Yes'
- l2: fully supported
- l3: ''
- - class: spark
- l1: 'No'
- l2: ''
- l3: ''
-
- - name: Timers
- values:
- - class: model
- jira: BEAM-27
- l1: 'No'
- l2: delayed processing callbacks
- l3: A fine-grained mechanism for performing work at some point in the future, in either the event-time or processing-time domain. Useful for orchestrating delayed events, timeouts, etc in complex state per-key, per-window state machines.
- - class: dataflow
- l1: 'No'
- l2: pending model support
- l3: Dataflow already supports timers internally, so adding support for this should be easy once the Beam model exposes it.
- - class: flink
- l1: 'No'
- l2: pending model support
- l3: Flink already supports timers internally, so adding support for this should be easy once the Beam model exposes it.
- - class: spark
- l1: 'No'
- l2: pending model support
- l3: ''
-
-
- - description: How do refinements relate?
- anchor: how
- color-b: 'b55'
- color-y: 'd77'
- color-p: 'faa'
- color-n: 'ddd'
- rows:
-
- - name: Discarding
- values:
- - class: model
- l1: 'Yes'
- l2: panes discard elements when fired
- l3: Elements are discarded from accumulated state as their pane is fired.
- - class: dataflow
- l1: 'Yes'
- l2: fully supported
- l3: ''
- - class: flink
- l1: 'Yes'
- l2: fully supported
- l3: ''
- - class: spark
- l1: 'Yes'
- l2: fully supported
- l3: 'Spark streaming natively discards elements after firing.'
-
- - name: Accumulating
- values:
- - class: model
- l1: 'Yes'
- l2: panes accumulate elements across firings
- l3: Elements are accumulated in state across multiple pane firings for the same window.
- - class: dataflow
- l1: 'Yes'
- l2: fully supported
- l3: Requires that the accumulated pane fits in memory, after being passed through the combiner (if relevant)
- - class: flink
- l1: 'Yes'
- l2: fully supported
- l3: ''
- - class: spark
- l1: 'No'
- l2: ''
- l3: ''
-
- - name: 'Accumulating & Retracting'
- values:
- - class: model
- jira: BEAM-91
- l1: 'No'
- l2: accumulation plus retraction of old panes
- l3: Elements are accumulated across multiple pane firings and old emitted values are retracted. Also known as "backsies" ;-D
- - class: dataflow
- l1: 'No'
- l2: pending model support
- l3: ''
- - class: flink
- l1: 'No'
- l2: pending model support
- l3: ''
- - class: spark
- l1: 'No'
- l2: pending model support
- l3: ''
http://git-wip-us.apache.org/repos/asf/incubator-beam-site/blob/cc22c462/_data/logos.yml
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diff --git a/_data/logos.yml b/_data/logos.yml
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+++ /dev/null
@@ -1,8 +0,0 @@
-sizes: [100, 200, 500, 1000]
-colors:
- full-color: "Full color"
- 3-color: "Three color"
- bw: "Black and white"
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-events:
-- date: 2016/04/01
- time: "9:30 - 16:00 Pacific"
- location: PayPal<br>San Jose, CA, USA
- type: Dev/PPMC Meeting
- materials:
- - title: Presentation - PPMC Deep Dive
- link: "https://docs.google.com/presentation/d/1uTb7dx4-Y2OM_B0_3XF_whwAL2FlDTTuq2QzP9sJ4Mg/edit?usp=sharing"
-
- - title: Notes - PPMC Deep Dive
- link: "https://docs.google.com/document/d/1SXSLj7FMIgKqj43nTcczFpJzqASeUMUCpbyklk2fBkg/edit?usp=sharing"
- notes:
-
-- date: 2016/05/04
- time: "8:00 - 11:00 Pacific"
- location: Virtual
- type: Technical Deep Dive
- materials:
- - title: Presentation - Beam Community Meeting
- link: "https://drive.google.com/open?id=17i7SHViboWtLEZw27iabdMisPl987WWxvapJaXg_dEE"
-
- - title: Notes - Beam Community Meeting
- link: "https://drive.google.com/open?id=1szhEE_pfhEtrQye61jXAidUcMW7oebZCRc2InUe3ou0"
- notes:
-
-last_updated: 2016/05/16
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- function ToggleTables(showDetails, anchor) {
- document.getElementById("cap-summary").style.display = showDetails ? "none" : "block";
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- location.hash = anchor;
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-<div id='cap-{{ cap-view }}' style='display:{{ cap-display }}'>
-<table class='{{ cap-style }}'>
- {% for category in cap-data.categories %}
- <tr class='{{ cap-style }}' id='cap-{{ cap-view }}-{{ category.anchor }}'>
- <th class='{{ cap-style }} color-metadata format-category' colspan='5' style='color:#{{ category.color-b }}'>{% if cap-view != 'blog' %}<div class='cap-toggle' onclick='ToggleTables({{ cap-toggle-details }}, "cap-{{ cap-other-view }}-{{ category.anchor }}")'>({% if cap-toggle-details == 1 %}expand{% else %}collapse{% endif %} details)</div>{% endif %}{{ category.description }}</th>
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- <th class='{{ cap-style }} color-platform format-platform' style='color:#{{ category.color-y }}'>{{ x.name }}</th>
- {% endfor %}
- </tr>
- {% for row in category.rows %}
- <tr class='{{ cap-style }}'>
- <th class='{{ cap-style }} color-capability format-capability' style='color:#{{ category.color-y }}'>{{ row.name }}</th>
- {% for val in row.values %}
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-
- <td width='25%' class='{{ cap-style }}' style='background-color:#{% if val.l1 == 'Yes' %}{{ category.color-y }}{% elsif val.l1 == 'Partially' %}{{ category.color-p }}{% else %}{{ category.color-n }}{% endif %};border-color:#{{category.color-b}}'>{{ value-markdown }}</td>
- {% endfor %}
- </tr>
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- <tr class='{{ cap-style }}'>
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http://git-wip-us.apache.org/repos/asf/incubator-beam-site/blob/cc22c462/_layouts/v2home.html
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http://git-wip-us.apache.org/repos/asf/incubator-beam-site/blob/cc22c462/_posts/2016-02-22-beam-has-a-logo.markdown
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----
-layout: post
-title: "Apache Beam has a logo!"
-date: 2016-02-22 10:21:48 -0800
-excerpt_separator: <!--more-->
-categories: beam update website
-authors:
-- jamesmalone
----
-
-One of the major benefits of Apache Beam is the fact that it unifies both
-both batch and stream processing into one powerful model. In fact, this unification
-is so important, the name Beam itself comes from the union of **B**atch + str**EAM** = Beam
-
-When the project started, we wanted a logo which was both appealing and visually
-represented this unification. <!--more--> Thanks to the **amazing** work of Stephanie Smythies, the Apache Beam project
-now has a logo.
-
-*drum roll* - **Presenting, the Apache Beam Logo!**
-
-<img src="{{ "/images/beam_logo_s.png" | prepend: site.baseurl }}" alt="Apache Beam Logo">
-
-We are excited about this logo because it is **simple**, **bright**, and shows the
-unification of bath and streaming, as beams of light, within the 'B'. We will base
-our future website and documentation design around this logo and its coloring. We
-will also make various permutations and resolutions of this logo available in the
-coming weeks. For any questions or comments, send an email to the `dev@` email list
-for Apache Beam.
http://git-wip-us.apache.org/repos/asf/incubator-beam-site/blob/cc22c462/_posts/2016-02-25-python-sdk-now-public.markdown
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----
-layout: post
-title: "Dataflow Python SDK is now public!"
-date: 2016-02-25 13:00:00 -0800
-excerpt_separator: <!--more-->
-categories: beam python sdk
-authors:
-- jamesmalone
----
-
-When the Apache Beam project proposed entry into the [Apache Incubator](http://wiki.apache.org/incubator/BeamProposal) the proposal
-included the [Dataflow Java SDK](https://github.com/GoogleCloudPlatform/DataflowJavaSDK). In the long term, however, Apache Beam aims to support SDKs implemented in multiple languages, such as Python.
-
-<!--more-->
-
-Today, Google submitted the [Dataflow Python (2.x) SDK](http://github.com/GoogleCloudPlatform/DataflowPythonSDK) on GitHub. Google is committed to including the in progress python SDK in Apache Beam and, in that spirit, we've moved development of the Python SDK to a public repository. While this SDK will not be included with the initial (incubating) releases of Apache Beam, our we plan on incorporating the Python SDK into beam during incubation. We want to take the time to implement changes from the [technical vision](https://goo.gl/nk5OM0) into the Java SDK before we introduce a Python SDK for Apache Beam. We believe this will allow us to work on the model and SDKs in an ordered fashion.
-
-You can look for the Apache Beam Python SDK in the coming months once we finish forking and refactoring the Java SDK.
-
-Best,
-
-Apache Beam Team