Query Interface

SQL

RubatoDB fully supports SQL.

Storage Architecture

Disk-oriented

RubatoDB has employed a hybrid storage partition model that allows the partitioning of a table both in the horizontal and the vertical dimension and being stored separately over the network of grid nodes. All disk accesses are made through Berkeley DB as all the partitions are stored as Berkeley DB files. The user can specify partitioning schemes to incorporate human optimizations based on precursory knowledge of the workload. A tree based schema is present for Grid Partitioning. Descendant tuples are partitioned according to the ancestor they descended from, ie for every row in the parent table there must be a/a group of rows in the descendant table.

Indexes

B+Tree

RubatoDB supports B+ Tree Indexes.

Data Model

Key/Value

RubatoDB is a Key-Value store due to the underlying data model of Berkeley DB. Support is provided for variable length keys and access through BTree Indexes.

Isolation Levels

Serializable

System Architecture

Shared-Nothing

RubatoDB follows a Staged Event-Driven Architecture. The individual tasks are divided into Finite State Machines and the transitions between the states of the FSM are triggered by events. The architecture can be visualized as a network of nodes acting as staged modules connected by queues which are explicitly associated. SE-DA breaks the execution plan into a series of stages where each stage corresponds to a subset of states from the FSM. This is now an independent identity with its own queue. It pulls tasks from the incoming queue, performs the operations and forwards it to the respective output queues.

Query Execution

Vectorized Model

The SQL engine present in RubatoDB is responsible for processing all queries. It is composed of a set of staged grip modules each comprising of a software module on a node having its own request queue. Threads pull requests one after the other from the input queue and invoke the various components, like parser, query optimizer, query processor, update etc. They then fill the output queue with the results which are then used up. This structure supports both parallelism and pipe lined execution.

Views

Not Supported

Since RubatoDB is not a relational database, there is no concept of views.

Checkpoints

Fuzzy

Fuzzy Check-pointing supported by Berkeley DB is present here too.

Concurrency Control

Multi-version Concurrency Control (MVCC)

Concurrency Control in RubatoDB is provided through two different layers :-

1. Transaction Stage - This is present in all grid servers and is responsible for data integrity at a per server level. It supports operations like pre_commit, commit and rollback.
2. FormulaDB - The FormulaDB layer is a distributed implementation of the Multi-Version Timestamp Concurrency Control Protocol. It is a thread free-layer present on the top over all the Berkeley DB nodes, orchestrating the transactions and allowing the system to provide three levels of consistency :-
3. ACID - The traditional Relational DBMS consistency guarantees, Atomicity, Consistency, Isolation and Durability.
4. BASE - This is one of models of the NoSQL world with weak consistency semantics. BASE stands for Basically Available with Soft State Eventual Consistency.
5. BASIC - RubatoDB also provides a middle ground of the both SQL and new SQL worlds, standing between the two extremes. BASIC stands for Basic Availability, Scalability and Instant Consistency.

The BASE and BASIC models differ on choosing one spectrum of the CAP theorem, either providing instant availability with fast queries or providing consistent results with higher latency.

Logging

Physical Logging

Each Berkeley DB node supports a WAL(Write Ahead LOG). This ensures that all data is not immediately written to disk, rather the transactions are made durable by appending the LOG records on the disk. Berkeley DB nodes follow traditional ARIES algorithm to persist the log on the disk and recover in the case of a crash.