Download Slack for free for mobile devices and desktop. Keep up with the conversation with our apps for iOS, Android, Mac, Windows and Linux. 5,735 downloads Updated: August 4, 2020 Freeware. Review Free Download specifications. DOWNLOAD Spark 2.8.2. This enables Disqus, Inc. To process some of your data. Disqus privacy policy. Spark 2.8.2 add to watchlist send us an update. Continue with Google. Continue with Facebook. Continue with Apple. Sign up with email. Log in with Adobe ID. The Spark amp and app work together to learn your style and feel, and then generate authentic bass and drums to accompany you. It’s a smart virtual band that goes wherever you go!.Auto Chords. Automatically display chords for millions of songs. Choose any song, and Spark will auto display its chords in real-time as you play.
Version: 2.7.2
Size: 47.96 MB
Date: June 22, 2020
Platform: Windows Vista/7/8/10
Intel® Pentium® 2.0 GHz or equivalent AMD
4 GB RAM
104.6 MB free HDD
1024 x 768 display
DirectX 9.0c or above
Recent changelog
Support for Canon EOS 850D / T8i / Kiss X10i, Canon EOS Ra
Support for microphones with 48KHz sampling rate
Fixed possible preview freezing on some Windows configurations
Added Mono mode for virtual microphone
Cannon mx12 scannor download for mac. Other bug fixes
Support for Nikon D780, Nikon D6
Movie Mode support for Canon cameras
Fixed recording bug when SparkoCam is minimized
Support for Canon EOS-1D X Mark III
Added ability for Canon cameras to show live view on camera's LCD
Fixed auto-focus issue with Canon cameras
Support for Canon EOS M6 Mark II / EOS 90D, Canon M200
Support for Canon PowerShot G7X Mark III, Canon PowerShot G5X Mark II
Support for Nikon Z50
Support for Canon EOS Rebel SL3/250D/EOS 200D II/Kiss X10
Support for Canon EOS RP
Support for Canon EOS R
Support for Nikon Z6, Nikon Z7
Quick search feature
Support for Canon M50, Canon T7/2000D/Kiss X90, Canon T100/3000D/4000D
4K Ultra-HD displays support
White balance and color correction features
Support for Nikon D850
Virtual microphone bug fixes
Support for Canon 800D / T7i / Kiss X9i, Canon 77D / 9000D
Support for Canon 6D Mark II, Canon SL2 / 200D / Kiss X9
Support for Nikon D5600, Nikon D7500
Download mac os x 10.13. Desktop streaming improvements
Some bug fixes and enhancements
Support for Canon 80D, Canon 1300D / T6, Canon 1D X Mark II, Canon 5D Mark IV
Support for Nikon D500, Nikon D5
Virtual microphone feature
Support for Canon 750D / T6i / Kiss X8i, Canon 760D / T6s / 8000D, Canon 5DS, Canon 5DS R
Support for Nikon D7200, Nikon D810A, Nikon 1 V3
Green screening improvements
Some bug fixes
Zoom/Crop/Rotate effect
Text over video
Fullscreen preview
Recording to AVI format
Picture-in-Picture feature
Ability to record system sound on Vista/7/8
Ability to save/load presets
Support for delayed recordings/snapshots
Support for QuickTime movies with alpha channel (requires having QuickTime installed)
Ability to use DSLR, Desktop or Webcam as a green screening background via PiP
Support for Canon 1200D, Canon 7D Mark II
Support for Nikon D5500, Nikon D750, Nikon D810, Nikon Df, Nikon D4s
Support for 64-bit applications
Fixed issue with color picker for 'Green Screening' mode
Fixed black live view issue with some Nikon cameras
Fixed issue with distorted images in green screening mode
Implemented possibility to apply effects to pictures taken by Canon / Nikon DSLR camera
Performance optimization on some system configurations
Support for Nikon D5300
Added ability to create custom 'Scene' effects
Fixed issue with distorted snapshots on certain video resolutions
Implemented automatic live view restarting for Nikon DSLRs to prevent camera's 'Auto Off'
Improvements for 'Desktop' mode
Support for Nikon DSLR cameras
Added 'Mirror Webcam Video' feature
Added 'Depth of Field' option for Canon Camera mode
Added ability to automatically disable all effects when in Desktop mode
Added ability to choose resizing algorithm for video frames
Implemented runtime detection of attached/detached webcams
Spark Free Download For Pc
Fixed dropped frames issue with Canon DSLR cameras after several minutes of using the app on certain configurations
Performance improvements on high video resolutions
Added 'Desktop' mode for streaming desktop screen as webcam
Fixed issue with SparkoCam logo in preview when switching video sources
Support for Canon 70D
Download Spark 2.3 On Macbook
Added time indicator during video recording
Fixed conflict issue with 'Samsung AllShare' application causing a black screen in preview
Added ability to use video file as a background for green screen mode
Added 'Restart Live View' option to prevent DSLR camera going into sleep mode
Support for the EOS Kiss X7i / EOS 700D / EOS REBEL T5i, EOS Kiss X7 / EOS 100D / EOS REBEL SL1
Mac Spark Install
Small performance enhancements for 'Video' mode
Fixed auto-shutdown issue with Canon DSLR cameras
Spark Release 2.3.0
Apache Spark 2.3.0 is the fourth release in the 2.x line. This release adds support for Continuous Processing in Structured Streaming along with a brand new Kubernetes Scheduler backend. Other major updates include the new DataSource and Structured Streaming v2 APIs, and a number of PySpark performance enhancements. In addition, this release continues to focus on usability, stability, and polish while resolving around 1400 tickets.
To download Apache Spark 2.3.0, visit the downloads page. You can consult JIRA for the detailed changes. We have curated a list of high level changes here, grouped by major modules.
Core, PySpark and Spark SQL
- Major features
- Spark on Kubernetes: [SPARK-18278] A new kubernetes scheduler backend that supports native submission of spark jobs to a cluster managed by kubernetes. Note that this support is currently experimental and behavioral changes around configurations, container images and entrypoints should be expected.
- Vectorized ORC Reader: [SPARK-16060] Adds support for new ORC reader that substantially improves the ORC scan throughput through vectorization (2-5x). To enable the reader, users can set
spark.sql.orc.impl
tonative
. - Spark History Server V2: [SPARK-18085] A new spark history server (SHS) backend that provides better scalability for large scale applications with a more efficient event storage mechanism.
- Data source API V2: [SPARK-15689][SPARK-22386] An experimental API for plugging in new data sources in Spark. The new API attempts to address several limitations of the V1 API and aims to facilitate development of high performant, easy-to-maintain, and extensible external data sources. Note that this API is still undergoing active development and breaking changes should be expected.
- PySpark Performance Enhancements: [SPARK-22216][SPARK-21187] Significant improvements in python performance and interoperability by fast data serialization and vectorized execution.
- Performance and stability
- [SPARK-21975] Histogram support in cost-based optimizer
- [SPARK-20331] Better support for predicate pushdown for Hive partition pruning
- [SPARK-19112] Support for ZStandard compression codec
- [SPARK-21113] Support for read ahead input stream to amortize disk I/O cost in the spill reader
- [SPARK-22510][SPARK-22692][SPARK-21871] Further stabilize the codegen framework to avoid hitting the
64KB
JVM bytecode limit on the Java method and Java compiler constant pool limit - [SPARK-23207] Fixed a long standing bug in Spark where consecutive shuffle+repartition on a DataFrame could lead to incorrect answers in certain surgical cases
- [SPARK-22062][SPARK-17788][SPARK-21907] Fix various causes of OOMs
- [SPARK-22489][SPARK-22916][SPARK-22895][SPARK-20758][SPARK-22266][SPARK-19122][SPARK-22662][SPARK-21652] Enhancements in rule-based optimizer and planner
- Other notable changes
- [SPARK-20236] Support Hive style dynamic partition overwrite semantics.
- [SPARK-4131] Support
INSERT OVERWRITE DIRECTORY
to directly write data into the filesystem from a query - [SPARK-19285][SPARK-22945][SPARK-21499][SPARK-20586][SPARK-20416][SPARK-20668] UDF enhancements
- [SPARK-20463][SPARK-19951][SPARK-22934][SPARK-21055][SPARK-17729][SPARK-20962][SPARK-20963][SPARK-20841][SPARK-17642][SPARK-22475][SPARK-22934] Improved ANSI SQL compliance and Hive compatibility
- [SPARK-20746] More comprehensive SQL built-in functions
- [SPARK-21485] Spark SQL documentation generation for built-in functions
- [SPARK-19810] Remove support for Scala
2.10
- [SPARK-22324] Upgrade Arrow to
0.8.0
and Netty to4.1.17
Programming guides: Spark RDD Programming Guide and Spark SQL, DataFrames and Datasets Guide.
Structured Streaming
- Continuous Processing
- A new execution engine that can execute streaming queries with sub-millisecond end-to-end latency by changing only a single line of user code. To learn more see the programming guide.
- Stream-Stream Joins
- Ability to join two streams of data, buffering rows until matching tuples arrive in the other stream. Predicates can be used against event time columns to bound the amount of state that needs to be retained.
- Streaming API V2
- An experimental API for plugging in new source and sinks that works for batch, micro-batch, and continuous execution. Note this API is still undergoing active development and breaking changes should be expected.
Programming guide: Structured Streaming Programming Guide.
MLlib
- Highlights
- ML Prediction now works with Structured Streaming, using updated APIs. Details below.
- New/Improved APIs
- [SPARK-21866]: Built-in support for reading images into a DataFrame (Scala/Java/Python)
- [SPARK-19634]: DataFrame functions for descriptive summary statistics over vector columns (Scala/Java)
- [SPARK-14516]:
ClusteringEvaluator
for tuning clustering algorithms, supporting Cosine silhouette and squared Euclidean silhouette metrics (Scala/Java/Python) - [SPARK-3181]: Robust linear regression with Huber loss (Scala/Java/Python)
- [SPARK-13969]:
FeatureHasher
transformer (Scala/Java/Python) - Multiple column support for several feature transformers:
- [SPARK-13030]:
OneHotEncoderEstimator
(Scala/Java/Python) - [SPARK-22397]:
QuantileDiscretizer
(Scala/Java) - [SPARK-20542]:
Bucketizer
(Scala/Java/Python)
- [SPARK-13030]:
- [SPARK-21633] and SPARK-21542]: Improved support for custom pipeline components in Python.
- New Features
- [SPARK-21087]:
CrossValidator
andTrainValidationSplit
can collect all models when fitting (Scala/Java). This allows you to inspect or save all fitted models. - [SPARK-19357]: Meta-algorithms
CrossValidator
,TrainValidationSplit,
OneVsRest` support a parallelism Param for fitting multiple sub-models in parallel Spark jobs - [SPARK-17139]: Model summary for multinomial logistic regression (Scala/Java/Python)
- [SPARK-18710]: Add offset in GLM
- [SPARK-20199]: Added
featureSubsetStrategy
Param toGBTClassifier
andGBTRegressor
. Using this to subsample features can significantly improve training speed; this option has been a key strength ofxgboost
.
- [SPARK-21087]:
- Other Notable Changes
- [SPARK-22156] Fixed
Word2Vec
learning rate scaling withnum
iterations. The new learning rate is set to match the originalWord2Vec
C code and should give better results from training. - [SPARK-22289] Add
JSON
support for Matrix parameters (This fixed a bug for ML persistence withLogisticRegressionModel
when using bounds on coefficients.) - [SPARK-22700]
Bucketizer.transform
incorrectly drops row containingNaN
. When ParamhandleInvalid
was set to “skip,”Bucketizer
would drop a row with a valid value in the input column if another (irrelevant) column had aNaN
value. - [SPARK-22446] Catalyst optimizer sometimes caused
StringIndexerModel
to throw an incorrect “Unseen label” exception whenhandleInvalid
was set to “error.” This could happen for filtered data, due to predicate push-down, causing errors even after invalid rows had already been filtered from the input dataset. - [SPARK-21681] Fixed an edge case bug in multinomial logistic regression that resulted in incorrect coefficients when some features had zero variance.
- Major optimizations:
- [SPARK-22707] Reduced memory consumption for
CrossValidator
- [SPARK-22949] Reduced memory consumption for
TrainValidationSplit
- [SPARK-21690]
Imputer
should train using a single pass over the data - [SPARK-14371]
OnlineLDAOptimizer
avoids collecting statistics to the driver for each mini-batch.
- [SPARK-22707] Reduced memory consumption for
- [SPARK-22156] Fixed
Programming guide: Machine Learning Library (MLlib) Guide.
SparkR
The main focus of SparkR in the 2.3.0 release was towards improving the stability of UDFs and adding several new SparkR wrappers around existing APIs:
- Major features
- Improved function parity between SQL and R
- [SPARK-22933]: Structured Streaming APIs for
withWatermark
,trigger
,partitionBy
and stream-stream joins - [SPARK-21266]: SparkR UDF with DDL-formatted schema support
- [SPARK-20726][SPARK-22924][SPARK-22843] Several new Dataframe API Wrappers
- [SPARK-15767][SPARK-21622][SPARK-20917][SPARK-20307][SPARK-20906] Several new SparkML API Wrappers
Programming guide: SparkR (R on Spark).
GraphX
- Optimizations
- [SPARK-5484] Pregel now checkpoints periodically to avoid
StackOverflowErrors
- [SPARK-21491] Small performance improvement in several places
- [SPARK-5484] Pregel now checkpoints periodically to avoid
Programming guide: GraphX Programming Guide.
Deprecations
- Python
- [SPARK-23122] Deprecate
register*
for UDFs inSQLContext
andCatalog
in PySpark
- [SPARK-23122] Deprecate
- MLlib
- [SPARK-13030]
OneHotEncoder
has been deprecated and will be removed in 3.0. It has been replaced by the newOneHotEncoderEstimator
. Note thatOneHotEncoderEstimator
will be renamed toOneHotEncoder
in 3.0 (butOneHotEncoderEstimator
will be kept as an alias).
- [SPARK-13030]
Changes of behavior
- SparkSQL
- [SPARK-22036]: By default arithmetic operations between decimals return a rounded value if an exact representation is not possible (instead of returning
NULL
in the prior versions) - [SPARK-22937]: When all inputs are binary, SQL
elt()
returns an output as binary. Otherwise, it returns as a string. In the prior versions, it always returns as a string despite of input types. - [SPARK-22895]: The Join/Filter’s deterministic predicates that are after the first non-deterministic predicates are also pushed down/through the child operators, if possible. In the prior versions, these filters were not eligible for predicate pushdown.
- [SPARK-22771]: When all inputs are binary,
functions.concat()
returns an output as binary. Otherwise, it returns as a string. In the prior versions, it always returns as a string despite of input types. - [SPARK-22489]: When either of the join sides is broadcastable, we prefer to broadcasting the table that is explicitly specified in a broadcast hint.
- [SPARK-22165]: Partition column inference previously found incorrect common type for different inferred types, for example, previously it ended up with
double
type as the common type fordouble
type anddate
type. Now it finds the correct common type for such conflicts. For details, see the migration guide. - [SPARK-22100]: The
percentile_approx
function previously acceptednumeric
type input and outputteddouble
type results. Now it supportsdate
type,timestamp
type andnumeric
types as input types. The result type is also changed to be the same as the input type, which is more reasonable for percentiles. - [SPARK-21610]: the queries from raw JSON/CSV files are disallowed when the referenced columns only include the internal corrupt record column (named
_corrupt_record
by default). Instead, you can cache or save the parsed results and then send the same query. - [SPARK-23421]: Since Spark 2.2.1 and 2.3.0, the schema is always inferred at runtime when the data source tables have the columns that exist in both partition schema and data schema. The inferred schema does not have the partitioned columns. When reading the table, Spark respects the partition values of these overlapping columns instead of the values stored in the data source files. In 2.2.0 and 2.1.x release, the inferred schema is partitioned but the data of the table is invisible to users (i.e., the result set is empty).
- [SPARK-22036]: By default arithmetic operations between decimals return a rounded value if an exact representation is not possible (instead of returning
- PySpark
- [SPARK-19732]:
na.fill()
orfillna
also accepts boolean and replaces nulls with booleans. In prior Spark versions, PySpark just ignores it and returns the original Dataset/DataFrame. - [SPARK-22395]: Pandas
0.19.2
or upper is required for using Pandas related functionalities, such astoPandas
,createDataFrame
from Pandas DataFrame, etc. - [SPARK-22395]: The behavior of timestamp values for Pandas related functionalities was changed to respect session timezone, which is ignored in the prior versions.
- [SPARK-23328]:
df.replace
does not allow to omitvalue
whento_replace
is not a dictionary. Previously,value
could be omitted in the other cases and hadNone
by default, which is counter-intuitive and error prone.
- [SPARK-19732]:
- MLlib
- Breaking API Changes: The class and trait hierarchy for logistic regression model summaries was changed to be cleaner and better accommodate the addition of the multi-class summary. This is a breaking change for user code that casts a
LogisticRegressionTrainingSummary
to aBinaryLogisticRegressionTrainingSummary
. Users should instead use themodel.binarySummary
method. See [SPARK-17139] for more detail (note this is an@Experimental
API). This does not affect the Python summary method, which will still work correctly for both multinomial and binary cases. - [SPARK-21806]:
BinaryClassificationMetrics.pr()
: first point (0.0, 1.0) is misleading and has been replaced by (0.0, p) where precision p matches the lowest recall point. - [SPARK-16957]: Decision trees now use weighted midpoints when choosing split values. This may change results from model training.
- [SPARK-14657]:
RFormula
without an intercept now outputs the reference category when encoding string terms, in order to match native R behavior. This may change results from model training. - [SPARK-21027]: The default parallelism used in
OneVsRest
is now set to 1 (i.e. serial). In 2.2 and earlier versions, the level of parallelism was set to the default threadpool size in Scala. This may change performance. - [SPARK-21523]: Upgraded Breeze to
0.13.2
. This included an important bug fix in strong Wolfe line search for L-BFGS. - [SPARK-15526]: The JPMML dependency is now shaded.
- Also see the “Bug fixes” section for behavior changes resulting from fixing bugs.
- Breaking API Changes: The class and trait hierarchy for logistic regression model summaries was changed to be cleaner and better accommodate the addition of the multi-class summary. This is a breaking change for user code that casts a
Known Issues
- [SPARK-23523][SQL] Incorrect result caused by the rule
OptimizeMetadataOnlyQuery
- [SPARK-23406] Bugs in stream-stream self-joins
Credits
Last but not least, this release would not have been possible without the following contributors:ALeksander Eskilson, Adrian Ionescu, Ajay Saini, Ala Luszczak, Albert Jang, Alberto Rodriguez De Lema, Alex Mikhailau, Alexander Istomin, Anderson Osagie, Andrea Zito, Andrew Ash, Andrew Korzhuev, Andrew Ray, Anirudh Ramanathan, Anton Okolnychyi, Arman Yazdani, Armin Braun, Arseniy Tashoyan, Arthur Rand, Atallah Hezbor, Attila Zsolt Piros, Ayush Singh, Bago Amirbekian, Ben Barnard, Bo Meng, Bo Xu, Bogdan Raducanu, Brad Kaiser, Bravo Zhang, Bruce Robbins, Bruce Xu, Bryan Cutler, Burak Yavuz, Carson Wang, Chang Chen, Charles Chen, Cheng Wang, Chenjun Zou, Chenzhao Guo, Chetan Khatri, Chie Hayashida, Chin Han Yu, Chunsheng Ji, Corey Woodfield, Daniel Li, Daniel Van Der Ende, Devaraj K, Dhruve Ashar, Dilip Biswal, Dmitry Parfenchik, Donghui Xu, Dongjoon Hyun, Eren Avsarogullari, Eric Vandenberg, Erik LaBianca, Eyal Farago, Favio Vazquez, Felix Cheung, Feng Liu, Feng Zhu, Fernando Pereira, Fokko Driesprong, Gabor Somogyi, Gene Pang, Gera Shegalov, German Schiavon, Glen Takahashi, Greg Owen, Grzegorz Slowikowski, Guilherme Berger, Guillaume Dardelet, Guo Xiao Long, He Qiao, Henry Robinson, Herman Van Hovell, Hideaki Tanaka, Holden Karau, Huang Tengfei, Huaxin Gao, Hyukjin Kwon, Ilya Matiach, Imran Rashid, Iurii Antykhovych, Ivan Sadikov, Jacek Laskowski, JackYangzg, Jakub Dubovsky, Jakub Nowacki, James Thompson, Jan Vrsovsky, Jane Wang, Jannik Arndt, Jason Taaffe, Jeff Zhang, Jen-Ming Chung, Jia Li, Jia-Xuan Liu, Jin Xing, Jinhua Fu, Jirka Kremser, Joachim Hereth, John Compitello, John Lee, John O’Leary, Jorge Machado, Jose Torres, Joseph K. Bradley, Josh Rosen, Juliusz Sompolski, Kalvin Chau, Kazuaki Ishizaki, Kent Yao, Kento NOZAWA, Kevin Yu, Kirby Linvill, Kohki Nishio, Kousuke Saruta, Kris Mok, Krishna Pandey, Kyle Kelley, Li Jin, Li Yichao, Li Yuanjian, Liang-Chi Hsieh, Lijia Liu, Liu Shaohui, Liu Xian, Liyun Zhang, Louis Lyu, Lubo Zhang, Luca Canali, Maciej Brynski, Maciej Szymkiewicz, Madhukara Phatak, Mahmut CAVDAR, Marcelo Vanzin, Marco Gaido, Marcos P, Marcos P. Sanchez, Mark Petruska, Maryann Xue, Masha Basmanova, Miao Wang, Michael Allman, Michael Armbrust, Michael Gummelt, Michael Mior, Michael Patterson, Michael Styles, Michal Senkyr, Mikhail Sveshnikov, Min Shen, Ming Jiang, Mingjie Tang, Mridul Muralidharan, Nan Zhu, Nathan Kronenfeld, Neil Alexander McQuarrie, Ngone51, Nicholas Chammas, Nick Pentreath, Ohad Raviv, Oleg Danilov, Onur Satici, PJ Fanning, Parth Gandhi, Patrick Woody, Paul Mackles, Peng Meng, Peng Xiao, Pengcheng Liu, Peter Szalai, Pralabh Kumar, Prashant Sharma, Rekha Joshi, Remis Haroon, Reynold Xin, Reza Safi, Riccardo Corbella, Rishabh Bhardwaj, Robert Kruszewski, Ron Hu, Ruben Berenguel Montoro, Ruben Janssen, Rui Zha, Rui Zhan, Ruifeng Zheng, Russell Spitzer, Ryan Blue, Sahil Takiar, Saisai Shao, Sameer Agarwal, Sandor Murakozi, Sanket Chintapalli, Santiago Saavedra, Sathiya Kumar, Sean Owen, Sergei Lebedev, Sergey Serebryakov, Sergey Zhemzhitsky, Seth Hendrickson, Shane Jarvie, Shashwat Anand, Shintaro Murakami, Shivaram Venkataraman, Shixiong Zhu, Shuangshuang Wang, Sid Murching, Sital Kedia, Soonmok Kwon, Srinivasa Reddy Vundela, Stavros Kontopoulos, Steve Loughran, Steven Rand, Sujith, Sujith Jay Nair, Sumedh Wale, Sunitha Kambhampati, Suresh Thalamati, Susan X. Huynh, Takeshi Yamamuro, Takuya UESHIN, Tathagata Das, Tejas Patil, Teng Peng, Thomas Graves, Tim Van Wassenhove, Travis Hegner, Tristan Stevens, Tucker Beck, Valeriy Avanesov, Vinitha Gankidi, Vinod KC, Wang Gengliang, Wayne Zhang, Weichen Xu, Wenchen Fan, Wieland Hoffmann, Wil Selwood, Wing Yew Poon, Xiang Gao, Xianjin YE, Xianyang Liu, Xiao Li, Xiaochen Ouyang, Xiaofeng Lin, Xiaokai Zhao, Xiayun Sun, Xin Lu, Xin Ren, Xingbo Jiang, Yan Facai (Yan Fa Cai), Yan Kit Li, Yanbo Liang, Yash Sharma, Yinan Li, Yong Tang, Youngbin Kim, Yuanjian Li, Yucai Yu, Yuhai Cen, Yuhao Yang, Yuming Wang, Yuval Itzchakov, Zhan Zhang, Zhang A Peng, Zhaokun Liu, Zheng RuiFeng, Zhenhua Wang, Zuo Tingbing, brandonJY, caneGuy, cxzl25, djvulee, eatoncys, heary-cao, ho3rexqj, lizhaoch, maclockard, neoremind, peay, shaofei007, wangjiaochun, zenglinxi0615
Spark News Archive