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But with Infobright, such problems evaporate because it does all the work dynamically for you. The actual data in Infobright is stored as columns as previously mentioned. The columns themselves are divided into groups of 64K values called data packs, whose metadata is stored in the knowledge grid. When a query is submitted to Infobright for execution, the optimizer consults the knowledge grid in order to generate a rough idea of which data packs contain data needed for the result set of the query.
Queries run exceptionally fast when a there are relatively few data packs containing data in the result set, and b the optimizer is able to accurately identify the set of needed data packs. One way to think of this data distribution is something akin to auto data partitioning.
And again, this is a good thing. In some cases a query can be executed without looking at any data packs at all; only the knowledge grid is consulted, and such queries will execute instantaneously. Since the knowledge grid contains many aggregate values, and because aggregates are a common aspect of queries in data warehousing applications, it is not unusual for many data warehousing type queries to execute with little or no required processing examples follow below.
Use cases where Infobright may not perform as well include applications with highly normalized schemas and more random data distributions. This is because such data doesn't compress as well as data with clustered patterns and because the data in the query result sets are spread around the database so that large numbers of data packs need to be scanned.
All the tests below were executed on a Dell PowerEdge , with four Intel Xeon dual-core processors 3. Working with Infobright is pretty much the same as any other MySQL engine when it comes to creating tables — all you do is specify brighthouse as the engine type. For example:. So now onto the actual tests: the schema I used for the queries below was a standard data warehousing star schema representing a car sales database that can be depicted in a data model as follows:.
The above shows a couple of fairly big fact tables at 1 billion rows each, a larger historical fact table at a little over 8 billion rows, one medium-sized summary table 4 million rows , and a number of dimension tables that are fairly small in size except the 2.
The total physical size of the database is almost GB, but the actual raw size of the data was 1TB, so you can see the Infobright compression in action and that it delivers as promised. Not too bad at all. Infobright plows through the data just fine. Of course, there are plenty of other queries that could be tested, but the above will give you a feel for how Infobright performs for some typical analytic-styled queries.
There are certain limitations you need to be aware of right now with Infobright. Infobright can handle up to 32 concurrent queries at this time. DML insert , update , delete ; only available in the Enterprise edition only supports table-level locking, which could reduce concurrency if much DML occurs in an Infobright warehouse. Theoretically, an Infobright table can go up to trillion rows, but practically, 50 billion is the limit today — a count which greatly depends on the data row size and datatypes used i.
Internationalization support is lacking right now; UTF8 support planned first half of You cannot switch from other tables to Infobright or vice versa. I also hit a small bug on two of the queries above where substituting the 8 billion row table for the one billion row table caused a performance hit on the query everything else being equal.
Apparently, there is a known bug on the sorting algorithm that relates to the source data and is only experienced with large tables. Infobright is in the process of correcting it. You can download and try out the Community version of the Infobright storage engine at www. Not Supported. Infobright does not support explicit indexes.
Each DPN contains some statistic information such as max, min, sum derived from the tuples that it stores. The knowledge grid store more advanced information such as interdependence between multiple tables, multiple columns and helps to locate the needed DPN with little decompress data as much as possible. For example, suppose a query wants to find such data which the value of certain column is within a specific range.
Each query does not need to decompress relevant and irrelevant packs, and will only need to find other data in suspect packs. In this way, the DPN serves like the index. Also the knowledge grid also serves like index because it records the relationship between multiple tables. So for join search, the DBMS first uses information of DPN in both tables to find related data blocks, and then uses knowledge node to build the relationship between those data blocks.
Both DPN and the knowledge grid avoids the need to maintain an index. Read Committed. Nested Loop Join. Vectorized Model. Custom API. Infobright is a disk-oriented DBMS. The system automatically creates the internal knowledge grid structure. This knowledge grid is key structure of the system for query execution. Decomposition Storage Model Columnar. Infobright support stored procedures.
When using this language to define a stored procedure, use delimiter key word to define the procedure and change it back when the definition is finished. Infobright is shared-nothing DBMS and it does not rely on special hardware. It combines a columnar database and knowledge grid for optimizing analytics such as compressing, storing and retrieving data.
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