local1.conf

# ================
# SPARK PROPERTIES
# ================
# Properties to be passed to "spark-submit" command via "--properties-file" 
# argument. This setting has higher priority than spark-defaults.conf or 
# SparkConf setting in runtime (except for runtime control properties). 
# For more information and full list of available properties, see [1].
 
# Spark.master value defines the cluster manager:
#   local             ... standalone local mode, use single thread (no parallelism)
#   local[N]          ... standalone local mode, use N cores
#   local[*]          ... standalone local mode, use all available cores
#   spark://host:port ... standalone cluster mode
#   + other cluster managers supported by spark (yarn, mesos, ...) 
# For more information, see [2].
#spark.master local
spark.master local[1]
#spark.master spark://host:port
#spark.master yarn

#spark.submit.deployMode cluster
spark.submit.deployMode client

#spark.yarn.appMasterEnv.PYSPARK_PYTHON /opt/anaconda3/bin/python

# Name of the application
spark.app.name ArchiveProcessor

# Memory setting (see e.g. [3])
spark.executor.memory 5g
spark.driver.memory 3g
#spark.executor.cores 1
#spark.executor.instances 4
#spark.driver.maxResultSize 250m
#spark.memory.storageFraction 0.1

# All important files needed by the driver or executors
spark.files \
    stoplists/cs.txt,\
    stoplists/de.txt,\
    stoplists/en.txt,\
    stoplists/fr.txt,\
    stoplists/pl.txt,\
    stoplists/ru.txt,\
    stoplists/sk.txt,\
    NLTK_data/tokenizers/punkt/PY3/czech.pickle,\
    NLTK_data/tokenizers/punkt/PY3/english.pickle,\
    NLTK_data/tokenizers/punkt/PY3/french.pickle,\
    NLTK_data/tokenizers/punkt/PY3/german.pickle,\
    NLTK_data/tokenizers/punkt/PY3/polish.pickle,\
    pyfiles.zip,\
    schema.json
spark.submit.pyFiles \
    pyfiles.zip

# additional properties
spark.logConf true

# References:
#   [1]  http://spark.apache.org/docs/latest/configuration.html
#   [2]  http://spark.apache.org/docs/latest/submitting-applications.html#master-urls
#   [3]  https://blog.cloudera.com/how-to-tune-your-apache-spark-jobs-part-2/