Distributed Configuration

The MapD Distributed Cluster

A MapD distributed database consists of three components:

• An aggregator, which is a specialized MapD Core Database instance for managing the cluster
• One or more leaf nodes, each being a complete MapD Core Database instance for storing and querying data
• A String Dictionary Server, which is a centralized repository for all dictionary-encoded items

Conceptually, a MapD distributed database is horizontally sharded across n leaf nodes. Each leaf node holds one nth of the total dataset. Sharding currently is round-robin only. Queries and responses are orchestrated by a MapD Aggregator server.

The MapD Aggregator

Clients interact with the aggregator. The aggregator orchestrates execution of a query across the appropriate leaf nodes. The aggregator composes the steps of the query execution plan to send to each leaf node, and manages their results. The full query execution might require multiple iterations between the aggregator and leaf nodes before returning a result to the client.

A core feature of the MapD Core Database is backend, GPU-based rendering for data-rich charts such as point maps. When running as a distributed cluster, the backend rendering is distributed across all leaf nodes, and the aggregator composes the final image.

String Dictionary Server

The String Dictionary Server manages and allocates IDs for dictionary-encoded fields, ensuring that these IDs are consistent across the entire cluster.

The server creates a new ID for each new encoded value. For queries returning results from encoded fields, the IDs are automatically converted to the original values by the aggregator. Leaf nodes use the string dictionary for processing joins on encoded columns.

For moderately sized configurations, the String Dictionary Server can share a host with a leaf node. For larger clusters, this service can be configured to run on a small, separate CPU-only server.

Replicated Tables

A table is split by default to 1/nth of the complete dataset. When you create a table used to provide dimension information, you can improve performance by replicating its contents onto every leaf node using the partitions property. For example:

CREATE TABLE flights … WITH (PARTITIONS=’REPLICATED’)

This reduces the distribution overhead during query execution in cases where sharding is not possible or appropriate. This is most useful for relatively small, heavily used dimension tables.

Data Loading

You can load data to a MapD distributed cluster using a COPY FROM statement to load data to the aggregator, exactly as with MapD single-node processing. The aggregator distributes data evenly across the leaf nodes.

An Example MapD Distributed Cluster

This example uses four GPU-based machines, each with a combination of one or more CPUs and GPUs.

Install MapD server on each node. For larger deployments, you can have the install on a shared drive.

Set up the configuration file for the entire cluster. This file is the same for all nodes.

[
  {
    "host": "node1",
    "port": 19091,
    "role": "dbleaf"
  },
  {
    "host": "node2",
    "port": 19091,
    "role": "dbleaf"
  },
 {
    "host": "node3",
    "port": 19091,
    "role": "dbleaf"
  },
  {
    "host": "node4",
    "port": 19091,
    "role": "dbleaf"
  },

  {
    "host": "node2",
    "port": 10301,
    "role": "string"
  }
]