Configuring, Launching SnappyData Clusters¶

Before you configure the SnappyData cluster, check the system requirements. In case you have not yet provisioned SnappyData, you can follow the instructions here. TIBCO recommends that you have at least 8 GB of memory and 4 cores available even for simple experimentation with SnappyData.

Launching Single Node Cluster with Default Configuration¶

If you want to launch the cluster either on Amazon EC2 or on a Kubernetes cluster, you can follow the instructions listed here (AWS) and here (Kubernetes)

If you are launching on a single node, for example, on your laptop or on a linux server you have access to, you can do so using this simple command:

./sbin/snappy-start-all.sh

http://(localhost or hostname or machineIP):5050

By default, the cluster uses the following ports:

All the artifacts created such as the server - logs, metrics, and the database files are all stored in a folder called work in the product home directory. Click the individual member URLs on the dashboard to view the logs.

Also see:

• Connecting with JDBC

• Connecting with ODBC

• Submitting a Spark Job

Configuring and Launching a Multi-node Cluster¶

Provision SnappyData and ensure that all the nodes are setup appropriately. If all the nodes are SSH enabled and can share folders using NFS or some shared file system, you can proceed to Capacity Planning. A shared file system is not a requirement but preferred.

Step 1: Capacity Planning¶

You must consider the capacity required for storage capacity (in-memory and disk) as well as the computational capacity that is required (memory and CPU). You can skip this step if the volume of data is low and feel you have abundant resources for the client workload.

Planning Storage Capacity¶

SnappyData is optimized for managing all the data in the memory. When data cannot fit in the memory, it automatically overflows the data to disk. To achieve the highest possible performance, we recommend you go through this exercise below. The capacity required is dependent on several variables such as input data format, the data types in use, use of indexes, number of redundant copies in the cluster, cardinality of the individual columns (compression ratio can vary a lot) and so on.

A general rule of thumb for compressed data (say Parquet) is to configure about 1.5X the compressed size on disk. Else, you can also use the following steps:

1. Define your external tables to access the data sources. This only creates catalog entries in SnappyData. For example, when loading data from a folder with data in CSV format, you can do the following using the Snappy Shell:

        create external table t1 using csv options(inferSchema 'true', header 'true', path '<some folder or file containing the CSV data>') ;
                    -- Spark Scala/Java/Python API example is omitted for brevity.

More examples for loading data from external sources are available [here](../howto/load_data_from_external_data_stores.md)

1. Decide if you want to manage this data in a Columnar or Row format.

2. Load a sample of the data:

   create table t1_inmem using column as (select * from t1 where rand() < 0.1) ;
   

   This loads about 10% of the data from the external data source. Note that the fraction value (0.1, which is 10%) should be reduced when working with large data sets or increased when the data set is small. You must repeat this process for all the tables that you want to manage in the memory.

3. Check the dashboard to view the actual memory consumed. Simply extrapolate to 100% (10 times for the example above). The amount of disk space is about two times the memory space requirement.

Estimating Computation Capacity¶

TIBCO recommends to configure off-heap memory. Even when off-heap is configured, you must also configure enough JVM heap memory for Spark temporary caching, computations, and buffering when the data is loaded.

TIBCO recommends to allocate at least 1 GB of memory per core to JVM heap for computations up to a max of 12 GB.

For example, when running on 8 core servers, configure JVM heap to be 8 GB, while on 32 core servers a heap of 12 - 16 GB will suffice. By default, 50% of the off-heap memory is available as computational memory. While, you may not need this much computational capacity when large off-heap is configured, it is still recommended for reserving enough capacity if working with large data sets.

If you are going to run queries using jobs that run queries returning multiple GB of results using the Dataset/RDD.collect() then all the data will need to held in the heap of lead node so you must account for that requirement when planning the heap size of lead node.

More complex the analytical processing, especially large aggregations, greater the space requirement in off-heap. For example, if your per server off-heap storage need is 100 GB then, allocate an additional 30 GB of off-heap for computations. Even if your data set is small, you must still allocate at least a few Gigabytes of off-heap storage for computations.

Step 2: Configure Core Cluster Component Properties¶

Configuration files for locator, lead, and server should be created in the conf folder located in the SnappyData home directory with names locators, leads, and servers.

To do so, you can copy the existing template files servers.template, locators.template, leads.template, and rename them to servers, locators, leads. These files should contain the hostnames of the nodes (one per line) where you intend to start the member. You can modify the properties to configure individual members.

Configuring Core Lead properties¶

The following core properties must be set in the conf/leads file:

Example Configurations¶

In the following configuration, you set the heap size for the lead and specify the working directory location:

localhost   -dir=/opt/snappydata/data/lead -heap-size=6g

In the following configuration, you are specifying the number of cores to use on each server:

localhost -spark.executor.cores=16

Configuring Core Locator Properties¶

The following core properties must be set in the conf/locators file:

Configuring Core Server Properties¶

The following core properties must be set in the conf/servers file:

Configuration Examples¶

cat <product_home>/conf/servers
node1_hostname -dir=/nfs/opt/snappy-db1/server1 -heap-size=8g -memory-size=42g
node1_hostname -dir=/nfs/opt/snappy-db1/server2 -heap-size=8g -memory-size=42g

// You can launch more than one data server on a host

node2_hostname -dir=/nfs/opt/snappy-db1/server3 -heap-size=8g -memory-size=42g
node2_hostname -dir=/nfs/opt/snappy-db1/server4 -heap-size=8g -memory-size=42g

Configuration Reference¶

See Configuration Reference section for more details.

List of Properties¶

Refer SnappyData properties for complete list of properties.