Work Smarter, Not Harder
The Infobright database resolves complex analytic queries without the need for traditional indexes, data partitioning, projections, manual tuning or specific schemas. Instead, the Knowledge Grid architecture automatically creates and stores the information needed to quickly resolve these queries. Infobright organizes the data into 2 layers: the compressed data itself that is stored in segments called Data Packs, and information about the data which comprises the components of the Knowledge Grid. For each query, the Infobright Granular Engine uses the informa¬tion in the Knowledge Grid to determine which Data Packs are relevant to the query before decompressing any data.
Infobright technology is based on the following concepts:
• Column orientation
• Data Packs
• Knowledge Grid
• The Granular Computing Engine
Infobright is, at its core, is a highly compressed column-oriented database. This means that instead of the data being stored row-by-row, it is stored column-by-column. There are many advantages to column-orientation, including the ability to do more efficient data compression because each column stores a single data type (as opposed to rows that typically contain several data types), and allowing compression to be optimized for each particular data type. Infobright, which organizes each column into Data Packs (as described below) has greater compression than other column-oriented databases, as it applies a compression algortithm based on the contents of each Data pack, not just each column.
Most queries only involve a subset of the columns of the tables and so a column-oriented database focuses on retrieving only the data that is required.
Data Packs and the Knowledge Grid
Data is stored in 65K Data Packs. Data Pack Nodes contain a set of statistics about the data that is stored and compressed in each of the Data Packs. Knowledge Nodes provide a further set of metadata related to Data Packs or column relationships.
Together, Data Pack Nodes and Knowledge Nodes form the Knowledge Grid. Unlike traditional database indexes, they are not manually created, and require no ongoing "care and feeding". Instead, they are created and managed automatically by the system. In essence, they create a high level view of the entire content of the database. This is what makes Infobright so well-suited for ad hoc analytics, unlike other databases that require pre-work such as indexes, projections, partitioning or aggregate tables in order to deliver fast query performance. Granular Computing Engine
The Granular Engine processes queries uses the Knowledge Grid information to optimize query processing. The goal is to eliminate or significantly reduce the amount of data that needs to be decompressed and accessed to answer a query. IEE can often answer queries referencing only the Knowledge Grid information, (without having to read the data), which results in sub-second query response for those queries.