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4. Advanced Tips for Deployment
Docker volume for the data
For storing the data, it is advisable to use Docker volumes. For that, first create a volume using
docker volume create adata
When running the container, mount the volume to the destination
/adampro/data where all the data is stored.
docker run ... --mount source=adata,destination=/adampro/data ...
With that you may create your entities and fill these with data. When deleting the container, the volume is kept and can be attached to a new container.
For accessing the underlying storage engines directly, expose the necessary ports. Note that not all ports might be available (depending on the container you are using).
5890 for the ADAMpro grpc endpoint
9099 for the ADAMpro Web UI
4040 for the Apache Spark UI
50010 50020 50070 50075 50090 for Apache Hadoop (HDFS)
1988 for Apache Hadoop (MapReduce)
8030 8031 8032 8033 8040 8042 8088 for Apache Hadoop (Yarn)
5432 for PostgreSQL
8983 for Apache Solr
9042 for Apache Cassandra
10088 for the Spark Notebook
19999 for netdata
Adjust the available memory and number of threads
Adjust the environment variables
ADAMPRO_DRIVER_MEMORY (for the default container and the hdfs container, set to
ADAMPRO_MEMORY (for the selfcontained container, set to
2g), respectively, denoting how much memory is allocated to ADAMpro and Apache Spark, and
ADAMPRO_MASTER (set to
local), which sets the number of threads in the local deployment. Both parameters have very minimal default values.
Choose which storage engines to use
selfcontained container, you may choose which storage engines to start. You can do this by adjusting the following environmental variables:
Certain containers are configured to have a graceful shutdown. Use
docker stop --time 60 adampro
and give the container around 1 minute time to gracefully shutdown all services.