Databricks Runtime 4.0

Databricks released this image in March 2018.

The following release notes provide information about Databricks Runtime 4.0, powered by Apache Spark.

Changes and Improvements

  • The JSON data source now tries to auto-detect encoding instead of assuming it to be UTF-8. In cases where the auto-detection fails, users can specify the charset option to enforce a certain encoding. See Charset auto-detection.
  • Scoring and prediction using Spark MLlib pipelines in Structured Streaming is fully supported.
  • Databricks ML Model Export is fully supported. With this feature, you can train a Spark MLlib model on Databricks, export it with a function call, and use a Databricks library in the system of your choice to import the model and score new data. See ML Model Export.
  • A new Spark data source implementation offers scalable read/write access to Azure SQL Data Warehouse. See Spark - SQL DW Connector.
  • The schema of the from_json function is now always converted to a nullable one. In other words, all fields, including nested ones, are nullable. This ensures that the data is compatible with the schema, preventing corruption after writing the data to parquet when a field is missing in the data and the user-provided schema declares the field as non-nullable.
  • Upgraded some installed Python libraries:
    • futures: from 3.1.1 to 3.2.0
    • pandas: from 0.18.1 to 0.19.2
    • pyarrow: from 0.4.1 to 0.8.0
    • setuptools: from 38.2.3 to 38.5.1
    • tornado: 4.5.2 to 4.5.3
  • Upgraded several installed R libraries. See Installed R Libraries.
  • Upgraded AWS Java SDK from 1.11.126 to 1.11.253.
  • Upgraded SQL Server JDBC driver from 6.1.0.jre8 to 6.2.2.jre8.
  • Upgraded PostgreSQL JDBC driver from 9.4-1204-jdbc41 to 42.1.4.

Apache Spark

Databricks Runtime 4.0 includes Apache Spark 2.3.0.

Core, PySpark, and Spark SQL

Major features

  • 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 to native.
  • 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 highly-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

Other notable changes

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 that 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-21633] and SPARK-21542]: Improved support for custom pipeline components in Python.

New Features

  • [SPARK-21087]: CrossValidator and TrainValidationSplit 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 to GBTClassifier and GBTRegressor. Using this to subsample features can significantly improve training speed; this option has been a key strength of xgboost.

Other Notable Changes

  • [SPARK-22156]: Fixed Word2Vec learning rate scaling with num iterations. The new learning rate is set to match the original Word2Vec C code and should give better results from training.
  • [SPARK-22289]: Add JSON support for Matrix parameters (This fixed a bug for ML persistence with LogisticRegressionModel when using bounds on coefficients.)
  • [SPARK-22700]: Bucketizer.transform incorrectly drops row containing NaN. When Param handleInvalid was set to “skip,” Bucketizer would drop a row with a valid value in the input column if another (irrelevant) column had a NaN value.
  • [SPARK-22446]: Catalyst optimizer sometimes caused StringIndexerModel to throw an incorrect “Unseen label” exception when handleInvalid 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.

Programming guide: Machine Learning Library (MLlib) Guide.

SparkR

The main focus of SparkR in the 2.3.0 release was improving the stability of UDFs and adding several new SparkR wrappers around existing APIs:

Major features

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.

Programming guide: GraphX Programming Guide.

Deprecations

Python

  • [SPARK-23122]: Deprecate register* for UDFs in SQLContext and Catalog in PySpark

MLlib

  • [SPARK-13030]: OneHotEncoder has been deprecated and will be removed in 3.0. It has been replaced by the new OneHotEncoderEstimator. Note that OneHotEncoderEstimator will be renamed to OneHotEncoder in 3.0 (but OneHotEncoderEstimator will be kept as an alias).

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 prior versions, it always returned as a string regardless 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 returned as a string regardless of input types.
  • [SPARK-22489]: When either of the join sides is broadcastable, we prefer to broadcast 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 for double type and date type. Now it finds the correct common type for such conflicts. For details, see the migration guide.
  • [SPARK-22100]: The percentile_approx function previously accepted numeric type input and outputted double type results. Now it supports date type, timestamp type and numeric 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).

PySpark

  • [SPARK-19732]: na.fill() or fillna 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 as toPandas, 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 omit value when to_replace is not a dictionary. Previously, value could be omitted in the other cases and had None by default, which is counter-intuitive and error prone.

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 a BinaryLogisticRegressionTrainingSummary. Users should instead use the model.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.

Known Issues

  • [SPARK-23523][SQL]: Incorrect result caused by the rule OptimizeMetadataOnlyQuery.
  • [SPARK-23406]: Bugs in stream-stream self-joins.

Maintenance Updates

Maintenance updates made to Databricks Runtime 4.0 since its initial release include:

  • Sep 11, 2018
    • Filter reduction should handle null value correctly.
  • Aug 28, 2018
    • Fixed a bug in Databricks Delta Delete command that would incorrectly delete the rows where the condition evaluates to null.
  • Aug 23, 2018
    • Fixed nullable map issue in Parquet reader.
    • Fixed secret manager redaction when command partially succeed
    • Fixed an interaction between Databricks Delta and Pyspark which could cause transient read failures.
    • [SPARK-25081]Fixed a bug where ShuffleExternalSorter may access a released memory page when spilling fails to allocate memory.
    • [SPARK-25114]Fix RecordBinaryComparator when subtraction between two words is divisible by Integer.MAX_VALUE.
  • Aug 2, 2018
    • [SPARK-24452]Avoid possible overflow in int add or multiple.
    • [SPARK-24588]Streaming join should require HashClusteredPartitioning from children.
    • Fixed an issue that could cause mergeInto command to produce incorrect results.
    • [SPARK-24867][SQL] Add AnalysisBarrier to DataFrameWriter. SQL cache is not being used when using DataFrameWriter to write a DataFrame with UDF. This is a regression caused by the changes we made in AnalysisBarrier, since not all the Analyzer rules are idempotent.
    • [SPARK-24809]Serializing LongHashedRelation in executor may result in data error.
  • June 28, 2018
    • Fixed a bug that could cause incorrect query results when the name of a partition column used in a predicate differs from the case of that column in the schema of the table.
  • May 31, 2018
    • Fixed a bug affecting Spark SQL execution engine.
    • Improved error handling in Delta.
  • May 17, 2018
    • Bug fixes for Databricks secret management.
    • Improved stability on reading data stored in Azure Data Lake Store.
    • Fixed a bug affecting RDD caching.
    • Fixed a bug affecting Null-safe Equal in Spark SQL.
  • Apr 24, 2018
    • Upgraded Azure Data Lake Store SDK from 2.0.11 to 2.2.8 to improve the stability of access Azure Data Lake Store.
    • Fixed a bug affecting inserting partitioned Hive table with overwrite when spark.databricks.io.hive.fastwriter.enabled is false.
    • Fixed an issue that failed task serialization.
    • Improved Delta stability.
  • Mar 14, 2018
    • Prevent unnecessary metadata updates in writers into Delta.
    • Fixed a bug caused by a race condition that could, in rare circumstances, lead to loss of some output files.

System Environment

  • Operating System: Ubuntu 16.04.4 LTS
  • Java: 1.8.0_151
  • Scala: 2.11.8
  • Python: 2.7.12 (or 3.5.2 if Python 3 support is enabled)
  • R: R version 3.4.3 (2017-11-30)
  • For GPU clusters, the following NVIDIA GPU libraries are installed:
    • CUDA 8.0
    • CUDNN 6.0

Installed Python Libraries

Library Version Library Version Library Version
ansi2html 1.1.1 argparse 1.2.1 backports-abc 0.5
boto 2.42.0 boto3 1.4.1 botocore 1.4.70
brewer2mpl 1.4.1 certifi 2016.2.28 cffi 1.7.0
chardet 2.3.0 colorama 0.3.7 configobj 5.0.6
cryptography 1.5 cycler 0.10.0 Cython 0.24.1
decorator 4.0.10 docutils 0.14 enum34 1.1.6
et-xmlfile 1.0.1 freetype-py 1.0.2 funcsigs 1.0.2
fusepy 2.0.4 futures 3.2.0 ggplot 0.6.8
html5lib 0.999 idna 2.1 ipaddress 1.0.16
ipython 2.2.0 ipython-genutils 0.1.0 jdcal 1.2
Jinja2 2.8 jmespath 0.9.0 llvmlite 0.13.0
lxml 3.6.4 MarkupSafe 0.23 matplotlib 1.5.3
mpld3 0.2 msgpack-python 0.4.7 ndg-httpsclient 0.3.3
numba 0.28.1 numpy 1.11.1 openpyxl 2.3.2
pandas 0.19.2 pathlib2 2.1.0 patsy 0.4.1
pexpect 4.0.1 pickleshare 0.7.4 Pillow 3.3.1
pip 9.0.1 ply 3.9 prompt-toolkit 1.0.7
psycopg2 2.6.2 ptyprocess 0.5.1 py4j 0.10.3
pyarrow 0.8.0 pyasn1 0.1.9 pycparser 2.14
Pygments 2.1.3 PyGObject 3.20.0 pyOpenSSL 16.0.0
pyparsing 2.2.0 pypng 0.0.18 Python 2.7.12
python-dateutil 2.5.3 python-geohash 0.8.5 pytz 2016.6.1
requests 2.11.1 s3transfer 0.1.9 scikit-learn 0.18.1
scipy 0.18.1 scour 0.32 seaborn 0.7.1
setuptools 38.5.1 simplejson 3.8.2 simples3 1.0
singledispatch 3.4.0.3 six 1.10.0 statsmodels 0.6.1
tornado 4.5.3 traitlets 4.3.0 urllib3 1.19.1
virtualenv 15.0.1 wcwidth 0.1.7 wheel 0.30.0
wsgiref 0.1.2        

Installed R Libraries

Library Version Library Version Library Version
abind 1.4-5 assertthat 0.2.0 backports 1.1.1
base 3.4.3 BH 1.65.0-1 bindr 0.1
bindrcpp 0.2 bit 1.1-12 bit64 0.9-7
bitops 1.0-6 blob 1.1.0 boot 1.3-20
brew 1.0-6 broom 0.4.3 car 2.1-6
caret 6.0-77 chron 2.3-51 class 7.3-14
cluster 2.0.6 codetools 0.2-15 colorspace 1.3-2
commonmark 1.4 compiler 3.4.3 crayon 1.3.4
curl 3.0 CVST 0.2-1 data.table 1.10.4-3
datasets 3.4.3 DBI 0.7 ddalpha 1.3.1
DEoptimR 1.0-8 desc 1.1.1 devtools 1.13.4
dichromat 2.0-0 digest 0.6.12 dimRed 0.1.0
doMC 1.3.4 dplyr 0.7.4 DRR 0.0.2
foreach 1.4.3 foreign 0.8-69 gbm 2.1.3
ggplot2 2.2.1 git2r 0.19.0 glmnet 2.0-13
glue 1.2.0 gower 0.1.2 graphics 3.4.3
grDevices 3.4.3 grid 3.4.3 gsubfn 0.6-6
gtable 0.2.0 h2o 3.16.0.1 httr 1.3.1
hwriter 1.3.2 hwriterPlus 1.0-3 ipred 0.9-6
iterators 1.0.8 jsonlite 1.5 kernlab 0.9-25
KernSmooth 2.23-15 labeling 0.3 lattice 0.20-35
lava 1.5.1 lazyeval 0.2.1 littler 0.3.2
lme4 1.1-14 lubridate 1.7.1 magrittr 1.5
mapproj 1.2-5 maps 3.2.0 MASS 7.3-48
Matrix 1.2-11 MatrixModels 0.4-1 memoise 1.1.0
methods 3.4.3 mgcv 1.8-23 mime 0.5
minqa 1.2.4 mnormt 1.5-5 ModelMetrics 1.1.0
munsell 0.4.3 mvtnorm 1.0-6 nlme 3.1-131
nloptr 1.0.4 nnet 7.3-12 numDeriv 2016.8-1
openssl 0.9.9 parallel 3.4.3 pbkrtest 0.4-7
pkgconfig 2.0.1 pkgKitten 0.1.4 plogr 0.1-1
plyr 1.8.4 praise 1.0.0 pROC 1.10.0
prodlim 1.6.1 proto 1.0.0 psych 1.7.8
purrr 0.2.4 quantreg 5.34 R.methodsS3 1.7.1
R.oo 1.21.0 R.utils 2.6.0 R6 2.2.2
randomForest 4.6-12 RColorBrewer 1.1-2 Rcpp 0.12.14
RcppEigen 0.3.3.3.1 RcppRoll 0.2.2 RCurl 1.95-4.8
recipes 0.1.1 reshape2 1.4.2 rlang 0.1.4
robustbase 0.92-8 RODBC 1.3-15 roxygen2 6.0.1
rpart 4.1-12 rprojroot 1.2 Rserve 1.7-3
RSQLite 2.0 rstudioapi 0.7 scales 0.5.0
sfsmisc 1.1-1 sp 1.2-5 SparkR 2.3.0
SparseM 1.77 spatial 7.3-11 splines 3.4.3
sqldf 0.4-11 statmod 1.4.30 stats 3.4.3
stats4 3.4.3 stringi 1.1.6 stringr 1.2.0
survival 2.41-3 tcltk 3.4.3 TeachingDemos 2.10
testthat 1.0.2 tibble 1.3.4 tidyr 0.7.2
tidyselect 0.2.3 timeDate 3042.101 tools 3.4.3
utils 3.4.3 viridisLite 0.2.0 whisker 0.3-2
withr 2.1.0 xml2 1.1.1    

Installed Java and Scala libraries (Scala 2.11 cluster version)

Group ID Artifact ID Version
antlr antlr 2.7.7
com.amazonaws amazon-kinesis-client 1.7.3
com.amazonaws aws-java-sdk-autoscaling 1.11.253
com.amazonaws aws-java-sdk-cloudformation 1.11.253
com.amazonaws aws-java-sdk-cloudfront 1.11.253
com.amazonaws aws-java-sdk-cloudhsm 1.11.253
com.amazonaws aws-java-sdk-cloudsearch 1.11.253
com.amazonaws aws-java-sdk-cloudtrail 1.11.253
com.amazonaws aws-java-sdk-cloudwatch 1.11.253
com.amazonaws aws-java-sdk-cloudwatchmetrics 1.11.253
com.amazonaws aws-java-sdk-codedeploy 1.11.253
com.amazonaws aws-java-sdk-cognitoidentity 1.11.253
com.amazonaws aws-java-sdk-cognitosync 1.11.253
com.amazonaws aws-java-sdk-config 1.11.253
com.amazonaws aws-java-sdk-core 1.11.253
com.amazonaws aws-java-sdk-datapipeline 1.11.253
com.amazonaws aws-java-sdk-directconnect 1.11.253
com.amazonaws aws-java-sdk-directory 1.11.253
com.amazonaws aws-java-sdk-dynamodb 1.11.253
com.amazonaws aws-java-sdk-ec2 1.11.253
com.amazonaws aws-java-sdk-ecs 1.11.253
com.amazonaws aws-java-sdk-efs 1.11.253
com.amazonaws aws-java-sdk-elasticache 1.11.253
com.amazonaws aws-java-sdk-elasticbeanstalk 1.11.253
com.amazonaws aws-java-sdk-elasticloadbalancing 1.11.253
com.amazonaws aws-java-sdk-elastictranscoder 1.11.253
com.amazonaws aws-java-sdk-emr 1.11.253
com.amazonaws aws-java-sdk-glacier 1.11.253
com.amazonaws aws-java-sdk-iam 1.11.253
com.amazonaws aws-java-sdk-importexport 1.11.253
com.amazonaws aws-java-sdk-kinesis 1.11.253
com.amazonaws aws-java-sdk-kms 1.11.253
com.amazonaws aws-java-sdk-lambda 1.11.253
com.amazonaws aws-java-sdk-logs 1.11.253
com.amazonaws aws-java-sdk-machinelearning 1.11.253
com.amazonaws aws-java-sdk-opsworks 1.11.253
com.amazonaws aws-java-sdk-rds 1.11.253
com.amazonaws aws-java-sdk-redshift 1.11.253
com.amazonaws aws-java-sdk-route53 1.11.253
com.amazonaws aws-java-sdk-s3 1.11.253
com.amazonaws aws-java-sdk-ses 1.11.253
com.amazonaws aws-java-sdk-simpledb 1.11.253
com.amazonaws aws-java-sdk-simpleworkflow 1.11.253
com.amazonaws aws-java-sdk-sns 1.11.253
com.amazonaws aws-java-sdk-sqs 1.11.253
com.amazonaws aws-java-sdk-ssm 1.11.253
com.amazonaws aws-java-sdk-storagegateway 1.11.253
com.amazonaws aws-java-sdk-sts 1.11.253
com.amazonaws aws-java-sdk-support 1.11.253
com.amazonaws aws-java-sdk-swf-libraries 1.11.22
com.amazonaws aws-java-sdk-workspaces 1.11.253
com.amazonaws jmespath-java 1.11.253
com.carrotsearch hppc 0.7.2
com.chuusai shapeless_2.11 2.3.2
com.clearspring.analytics stream 2.7.0
com.databricks Rserve 1.8-3
com.databricks dbml-local_2.11 0.3.0-db1-spark2.3
com.databricks dbml-local_2.11-tests 0.3.0-db1-spark2.3
com.databricks jets3t 0.7.1-0
com.databricks.scalapb compilerplugin_2.11 0.4.15-9
com.databricks.scalapb scalapb-runtime_2.11 0.4.15-9
com.esotericsoftware kryo-shaded 3.0.3
com.esotericsoftware minlog 1.3.0
com.fasterxml classmate 1.0.0
com.fasterxml.jackson.core jackson-annotations 2.6.7
com.fasterxml.jackson.core jackson-core 2.6.7
com.fasterxml.jackson.core jackson-databind 2.6.7.1
com.fasterxml.jackson.dataformat jackson-dataformat-cbor 2.6.7
com.fasterxml.jackson.datatype jackson-datatype-joda 2.6.7
com.fasterxml.jackson.module jackson-module-paranamer 2.6.7
com.fasterxml.jackson.module jackson-module-scala_2.11 2.6.7.1
com.github.fommil jniloader 1.1
com.github.fommil.netlib core 1.1.2
com.github.fommil.netlib native_ref-java 1.1
com.github.fommil.netlib native_ref-java-natives 1.1
com.github.fommil.netlib native_system-java 1.1
com.github.fommil.netlib native_system-java-natives 1.1
com.github.fommil.netlib netlib-native_ref-linux-x86_64-natives 1.1
com.github.fommil.netlib netlib-native_system-linux-x86_64-natives 1.1
com.github.luben zstd-jni 1.3.2-2
com.github.rwl jtransforms 2.4.0
com.google.code.findbugs jsr305 2.0.1
com.google.code.gson gson 2.2.4
com.google.guava guava 15.0
com.google.protobuf protobuf-java 2.6.1
com.googlecode.javaewah JavaEWAH 0.3.2
com.h2database h2 1.3.174
com.jamesmurty.utils java-xmlbuilder 1.1
com.jcraft jsch 0.1.50
com.jolbox bonecp 0.8.0.RELEASE
com.mchange c3p0 0.9.5.1
com.mchange mchange-commons-java 0.2.10
com.microsoft.azure azure-data-lake-store-sdk 2.0.11
com.microsoft.sqlserver mssql-jdbc 6.2.2.jre8
com.ning compress-lzf 1.0.3
com.sun.mail javax.mail 1.5.2
com.thoughtworks.paranamer paranamer 2.8
com.trueaccord.lenses lenses_2.11 0.3
com.twitter chill-java 0.8.4
com.twitter chill_2.11 0.8.4
com.twitter parquet-hadoop-bundle 1.6.0
com.twitter util-app_2.11 6.23.0
com.twitter util-core_2.11 6.23.0
com.twitter util-jvm_2.11 6.23.0
com.typesafe config 1.2.1
com.typesafe.scala-logging scala-logging-api_2.11 2.1.2
com.typesafe.scala-logging scala-logging-slf4j_2.11 2.1.2
com.univocity univocity-parsers 2.5.9
com.vlkan flatbuffers 1.2.0-3f79e055
com.zaxxer HikariCP 2.4.1
commons-beanutils commons-beanutils 1.7.0
commons-beanutils commons-beanutils-core 1.8.0
commons-cli commons-cli 1.2
commons-codec commons-codec 1.10
commons-collections commons-collections 3.2.2
commons-configuration commons-configuration 1.6
commons-dbcp commons-dbcp 1.4
commons-digester commons-digester 1.8
commons-httpclient commons-httpclient 3.1
commons-io commons-io 2.4
commons-lang commons-lang 2.6
commons-logging commons-logging 1.1.3
commons-net commons-net 2.2
commons-pool commons-pool 1.5.4
info.ganglia.gmetric4j gmetric4j 1.0.7
io.airlift aircompressor 0.8
io.dropwizard.metrics metrics-core 3.1.5
io.dropwizard.metrics metrics-ganglia 3.1.5
io.dropwizard.metrics metrics-graphite 3.1.5
io.dropwizard.metrics metrics-healthchecks 3.1.5
io.dropwizard.metrics metrics-jetty9 3.1.5
io.dropwizard.metrics metrics-json 3.1.5
io.dropwizard.metrics metrics-jvm 3.1.5
io.dropwizard.metrics metrics-log4j 3.1.5
io.dropwizard.metrics metrics-servlets 3.1.5
io.netty netty 3.9.9.Final
io.netty netty-all 4.1.17.Final
io.prometheus simpleclient 0.0.16
io.prometheus simpleclient_common 0.0.16
io.prometheus simpleclient_dropwizard 0.0.16
io.prometheus simpleclient_servlet 0.0.16
io.prometheus.jmx collector 0.7
javax.activation activation 1.1.1
javax.annotation javax.annotation-api 1.2
javax.el javax.el-api 2.2.4
javax.jdo jdo-api 3.0.1
javax.servlet javax.servlet-api 3.1.0
javax.servlet.jsp jsp-api 2.1
javax.transaction jta 1.1
javax.validation validation-api 1.1.0.Final
javax.ws.rs javax.ws.rs-api 2.0.1
javax.xml.bind jaxb-api 2.2.2
javax.xml.stream stax-api 1.0-2
javolution javolution 5.5.1
jline jline 2.11
joda-time joda-time 2.9.3
log4j apache-log4j-extras 1.2.17
log4j log4j 1.2.17
net.hydromatic eigenbase-properties 1.1.5
net.iharder base64 2.3.8
net.java.dev.jets3t jets3t 0.9.4
net.razorvine pyrolite 4.13
net.sf.jpam jpam 1.1
net.sf.opencsv opencsv 2.3
net.sf.supercsv super-csv 2.2.0
net.sourceforge.f2j arpack_combined_all 0.1
org.acplt oncrpc 1.0.7
org.antlr ST4 4.0.4
org.antlr antlr-runtime 3.4
org.antlr antlr4-runtime 4.7
org.antlr stringtemplate 3.2.1
org.apache.ant ant 1.9.2
org.apache.ant ant-jsch 1.9.2
org.apache.ant ant-launcher 1.9.2
org.apache.arrow arrow-format 0.8.0
org.apache.arrow arrow-memory 0.8.0
org.apache.arrow arrow-vector 0.8.0
org.apache.avro avro 1.7.7
org.apache.avro avro-ipc 1.7.7
org.apache.avro avro-ipc-tests 1.7.7
org.apache.avro avro-mapred-hadoop2 1.7.7
org.apache.calcite calcite-avatica 1.2.0-incubating
org.apache.calcite calcite-core 1.2.0-incubating
org.apache.calcite calcite-linq4j 1.2.0-incubating
org.apache.commons commons-compress 1.4.1
org.apache.commons commons-crypto 1.0.0
org.apache.commons commons-lang3 3.5
org.apache.commons commons-math3 3.4.1
org.apache.curator curator-client 2.7.1
org.apache.curator curator-framework 2.7.1
org.apache.curator curator-recipes 2.7.1
org.apache.derby derby 10.12.1.1
org.apache.directory.api api-asn1-api 1.0.0-M20
org.apache.directory.api api-util 1.0.0-M20
org.apache.directory.server apacheds-i18n 2.0.0-M15
org.apache.directory.server apacheds-kerberos-codec 2.0.0-M15
org.apache.hadoop hadoop-annotations 2.7.3
org.apache.hadoop hadoop-auth 2.7.3
org.apache.hadoop hadoop-client 2.7.3
org.apache.hadoop hadoop-common 2.7.3
org.apache.hadoop hadoop-hdfs 2.7.3
org.apache.hadoop hadoop-mapreduce-client-app 2.7.3
org.apache.hadoop hadoop-mapreduce-client-common 2.7.3
org.apache.hadoop hadoop-mapreduce-client-core 2.7.3
org.apache.hadoop hadoop-mapreduce-client-jobclient 2.7.3
org.apache.hadoop hadoop-mapreduce-client-shuffle 2.7.3
org.apache.hadoop hadoop-yarn-api 2.7.3
org.apache.hadoop hadoop-yarn-client 2.7.3
org.apache.hadoop hadoop-yarn-common 2.7.3
org.apache.hadoop hadoop-yarn-server-common 2.7.3
org.apache.htrace htrace-core 3.1.0-incubating
org.apache.httpcomponents httpclient 4.5.4
org.apache.httpcomponents httpcore 4.4.8
org.apache.ivy ivy 2.4.0
org.apache.orc orc-core-nohive 1.4.1
org.apache.orc orc-mapreduce-nohive 1.4.1
org.apache.parquet parquet-column 1.8.2-databricks1
org.apache.parquet parquet-common 1.8.2-databricks1
org.apache.parquet parquet-encoding 1.8.2-databricks1
org.apache.parquet parquet-format 2.3.1
org.apache.parquet parquet-hadoop 1.8.2-databricks1
org.apache.parquet parquet-jackson 1.8.2-databricks1
org.apache.thrift libfb303 0.9.3
org.apache.thrift libthrift 0.9.3
org.apache.xbean xbean-asm5-shaded 4.4
org.apache.zookeeper zookeeper 3.4.6
org.bouncycastle bcprov-jdk15on 1.58
org.codehaus.jackson jackson-core-asl 1.9.13
org.codehaus.jackson jackson-jaxrs 1.9.13
org.codehaus.jackson jackson-mapper-asl 1.9.13
org.codehaus.jackson jackson-xc 1.9.13
org.codehaus.janino commons-compiler 3.0.8
org.codehaus.janino janino 3.0.8
org.datanucleus datanucleus-api-jdo 3.2.6
org.datanucleus datanucleus-core 3.2.10
org.datanucleus datanucleus-rdbms 3.2.9
org.eclipse.jetty jetty-client 9.3.20.v20170531
org.eclipse.jetty jetty-continuation 9.3.20.v20170531
org.eclipse.jetty jetty-http 9.3.20.v20170531
org.eclipse.jetty jetty-io 9.3.20.v20170531
org.eclipse.jetty jetty-jndi 9.3.20.v20170531
org.eclipse.jetty jetty-plus 9.3.20.v20170531
org.eclipse.jetty jetty-proxy 9.3.20.v20170531
org.eclipse.jetty jetty-security 9.3.20.v20170531
org.eclipse.jetty jetty-server 9.3.20.v20170531
org.eclipse.jetty jetty-servlet 9.3.20.v20170531
org.eclipse.jetty jetty-servlets 9.3.20.v20170531
org.eclipse.jetty jetty-util 9.3.20.v20170531
org.eclipse.jetty jetty-webapp 9.3.20.v20170531
org.eclipse.jetty jetty-xml 9.3.20.v20170531
org.fusesource.leveldbjni leveldbjni-all 1.8
org.glassfish.hk2 hk2-api 2.4.0-b34
org.glassfish.hk2 hk2-locator 2.4.0-b34
org.glassfish.hk2 hk2-utils 2.4.0-b34
org.glassfish.hk2 osgi-resource-locator 1.0.1
org.glassfish.hk2.external aopalliance-repackaged 2.4.0-b34
org.glassfish.hk2.external javax.inject 2.4.0-b34
org.glassfish.jersey.bundles.repackaged jersey-guava 2.22.2
org.glassfish.jersey.containers jersey-container-servlet 2.22.2
org.glassfish.jersey.containers jersey-container-servlet-core 2.22.2
org.glassfish.jersey.core jersey-client 2.22.2
org.glassfish.jersey.core jersey-common 2.22.2
org.glassfish.jersey.core jersey-server 2.22.2
org.glassfish.jersey.media jersey-media-jaxb 2.22.2
org.hibernate hibernate-validator 5.1.1.Final
org.iq80.snappy snappy 0.2
org.javassist javassist 3.18.1-GA
org.jboss.logging jboss-logging 3.1.3.GA
org.jdbi jdbi 2.63.1
org.joda joda-convert 1.7
org.jodd jodd-core 3.5.2
org.json4s json4s-ast_2.11 3.2.11
org.json4s json4s-core_2.11 3.2.11
org.json4s json4s-jackson_2.11 3.2.11
org.lz4 lz4-java 1.4.0
org.mariadb.jdbc mariadb-java-client 2.1.2
org.mockito mockito-all 1.9.5
org.objenesis objenesis 2.1
org.postgresql postgresql 42.1.4
org.roaringbitmap RoaringBitmap 0.5.11
org.rocksdb rocksdbjni 5.2.1
org.rosuda.REngine REngine 2.1.0
org.scala-lang scala-compiler_2.11 2.11.8
org.scala-lang scala-library_2.11 2.11.8
org.scala-lang scala-reflect_2.11 2.11.8
org.scala-lang scalap_2.11 2.11.8
org.scala-lang.modules scala-parser-combinators_2.11 1.0.2
org.scala-lang.modules scala-xml_2.11 1.0.5
org.scala-sbt test-interface 1.0
org.scalacheck scalacheck_2.11 1.12.5
org.scalanlp breeze-macros_2.11 0.13.2
org.scalanlp breeze_2.11 0.13.2
org.scalatest scalatest_2.11 2.2.6
org.slf4j jcl-over-slf4j 1.7.16
org.slf4j jul-to-slf4j 1.7.16
org.slf4j slf4j-api 1.7.16
org.slf4j slf4j-log4j12 1.7.16
org.spark-project.hive hive-beeline 1.2.1.spark2
org.spark-project.hive hive-cli 1.2.1.spark2
org.spark-project.hive hive-exec 1.2.1.spark2
org.spark-project.hive hive-jdbc 1.2.1.spark2
org.spark-project.hive hive-metastore 1.2.1.spark2
org.spark-project.spark unused 1.0.0
org.spire-math spire-macros_2.11 0.13.0
org.spire-math spire_2.11 0.13.0
org.springframework spring-core 4.1.4.RELEASE
org.springframework spring-test 4.1.4.RELEASE
org.tukaani xz 1.0
org.typelevel machinist_2.11 0.6.1
org.typelevel macro-compat_2.11 1.1.1
org.xerial sqlite-jdbc 3.8.11.2
org.xerial.snappy snappy-java 1.1.2.6
org.yaml snakeyaml 1.16
oro oro 2.0.8
software.amazon.ion ion-java 1.0.2
stax stax-api 1.0.1
xmlenc xmlenc 0.52