As a distributed DBMS

System configuration
In order to manage large amount of data, we must examine communications in loosely coupled parallel processing. Closely related data should be located in the same cluster and query processing plans are responsible for decreasing communications.
Kappa is composed of local DBMSs(LDBMSs), all of which manage one database. Each LDBMS has a full set of functions of DBMS. Functions of distributed transactions are implemented based on two phase commitment protocol to handle queries concerning multiple LDBMS.
Global information such as table names of a database is accessed by multiple LDBMSs, which can cause concentration of access to a server DBMS(SDBMS). Replicants of a SDBMS are created to prevent the congestion.
LDBMSs on each cluster do parallel processing suited for tightly coupled multi processors.
Data placement and parallel processing
Load of each cluster and communications among them must be balanced for efficient parallel processing. In case of DBMS, large amount of data are stored in secondary memories. Therefore, data placement is closely related to methods of parallel processing.
- Distribution
  Distribution of relations or tables is the simplest way of exploiting computational power of multiple processors. Tables must be distribued taking communications and load balance into account.
- Horizontal partition
  Horizontal partitioning is a method of bringing out parallelism. A relation is horizontally partitioned in records and distributed to different LDBMSs. Basically, the same operation is requested to each partitioned relation and the operational results are collected at last.
  This method is effective when a relation is too large to be operated in a cluster or when CPU bound operations such as data searching are important.
- Replication
  Replicated relations contribute availability and prevent database access from congestion.

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