Criticisms
Sybase IQ is optimized for data warehouse type applications, where data is added to databases but typically not modified much if at all, and the typical access is to search through the data.
The column based storage which enables good performance for reading through data somewhat slows down writing data (instead of just the last data object requiring updating with a row object, one data object per table column must be updated). Large volume data imports can still be reasonably efficient.
Performance in a transactional environment, where row records are accessed and updated regularly, is significantly worse than competing transactional row-based relational databases.
Sybase IQ is implemented at large organizations such as SEC.
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Column-oriented DBMS
Column-oriented DBMS
A column-oriented DBMS is a database management system (DBMS) which stores its content by column rather than by row. This has advantages for databases such as data warehouses and library catalogues, where aggregates are computed over large numbers of similar data items. [1]
A column-oriented DBMS is a database management system (DBMS) which stores its content by column rather than by row. This has advantages for databases such as data warehouses and library catalogues, where aggregates are computed over large numbers of similar data items. [1]
Description
Description
Simplified greatly, DBMS systems are challenged with storing tabular data made up of rows and columns. Further, the set of columns is generally fixed by program design, while the number of rows is variable through use of the program. In one simple example, DBMS data might contain the following table.
EmpId Lastname Firstname Salary
1 Smith Joe 40000
2 Jones Mary 50000
3 Johnson Cathy 44000
This simple table includes an employee identifier (EmpId), name fields (Lastname and Firstname) and a salary (Salary).
Modern computer systems simplify the details of underlying storage down to a one-dimensional storage space, using a single numerical address to refer to a location. This is true for random access memory, hard disk drives, and other computer storage media. DBMS systems therefore must orient their data by flattening it onto a one-dimensional address space. Combine the common expectation for a fixed number of columns and a variable number of rows with the mapping from two dimensions to one, and we arrive at a column or row oriented classification.
A row-oriented implementation of a DBMS would store every attribute of a given row in sequence, with the last entry of one row followed by the first entry of the next.
1,Smith,Joe,40000;2,Jones,Mary,50000;3,Johnson,Cathy,44000;
A column-oriented implementation of a DBMS would store every attribute of a given column in sequence, with the column values for the same column stored in sequence, with the end of one column followed by the beginning of the next.
1,2,3;Smith,Jones,Johnson;Joe,Mary,Cathy;40000,50000,44000;
It is important to recognize that modern DBMS systems are not so simply column-oriented or row-oriented as in the simple example above, since partitioning, indexing, caching, materializing views, OLAP cubes, and transactional systems such as write ahead logging or multiversion concurrency control all dramatically affect physical organization of DBMS data. That said, OLTP focused RDBMS systems are more row-oriented, while OLAP focused systems are a balance of row-oriented and column-oriented.
Simplified greatly, DBMS systems are challenged with storing tabular data made up of rows and columns. Further, the set of columns is generally fixed by program design, while the number of rows is variable through use of the program. In one simple example, DBMS data might contain the following table.
EmpId Lastname Firstname Salary
1 Smith Joe 40000
2 Jones Mary 50000
3 Johnson Cathy 44000
This simple table includes an employee identifier (EmpId), name fields (Lastname and Firstname) and a salary (Salary).
Modern computer systems simplify the details of underlying storage down to a one-dimensional storage space, using a single numerical address to refer to a location. This is true for random access memory, hard disk drives, and other computer storage media. DBMS systems therefore must orient their data by flattening it onto a one-dimensional address space. Combine the common expectation for a fixed number of columns and a variable number of rows with the mapping from two dimensions to one, and we arrive at a column or row oriented classification.
A row-oriented implementation of a DBMS would store every attribute of a given row in sequence, with the last entry of one row followed by the first entry of the next.
1,Smith,Joe,40000;2,Jones,Mary,50000;3,Johnson,Cathy,44000;
A column-oriented implementation of a DBMS would store every attribute of a given column in sequence, with the column values for the same column stored in sequence, with the end of one column followed by the beginning of the next.
1,2,3;Smith,Jones,Johnson;Joe,Mary,Cathy;40000,50000,44000;
It is important to recognize that modern DBMS systems are not so simply column-oriented or row-oriented as in the simple example above, since partitioning, indexing, caching, materializing views, OLAP cubes, and transactional systems such as write ahead logging or multiversion concurrency control all dramatically affect physical organization of DBMS data. That said, OLTP focused RDBMS systems are more row-oriented, while OLAP focused systems are a balance of row-oriented and column-oriented.
Benefits
Benefits
Comparisons between row-oriented and column-oriented systems are typically concerned with the efficiency of hard-disk access for a given workload, as seek time time is incredibly slow compared to the other delays in computers. Further, because seek time is improving at a slow rate relative to cpu power (see Moore's Law), this focus will likely continue on systems reliant on hard-disks for storage. Following is a set of over-simplified observations which attempt to paint a picture of the trade-offs between column and row oriented organizations.
1. column-oriented systems are more efficient when an aggregate needs to be computed over many rows but only for a notably smaller subset of all columns of data, because reading that smaller subset of data can be faster than reading all data.
2. column-oriented systems are more efficient when new values of a column are supplied for all rows at once, because that column data can be written efficiently and replace old column data without touching any other columns for the rows.
3. row-oriented systems are more efficient when many columns of a single row are required at the same time, and when row-size is relatively small, as the entire row can be retrieved with a single disk seek.
4. row-oriented systems are more efficient when writing a new row if all of the column data is supplied at the same time, as the entire row can be written with a single disk seek.
In practice, row oriented architectures are well-suited for OLTP-like workloads which are more heavily loaded with interactive transactions. Column stores are well-suited for OLAP-like workloads (eg. data warehouses) which typically involve a smaller number of highly complex queries over all data (possibly terabytes).
Comparisons between row-oriented and column-oriented systems are typically concerned with the efficiency of hard-disk access for a given workload, as seek time time is incredibly slow compared to the other delays in computers. Further, because seek time is improving at a slow rate relative to cpu power (see Moore's Law), this focus will likely continue on systems reliant on hard-disks for storage. Following is a set of over-simplified observations which attempt to paint a picture of the trade-offs between column and row oriented organizations.
1. column-oriented systems are more efficient when an aggregate needs to be computed over many rows but only for a notably smaller subset of all columns of data, because reading that smaller subset of data can be faster than reading all data.
2. column-oriented systems are more efficient when new values of a column are supplied for all rows at once, because that column data can be written efficiently and replace old column data without touching any other columns for the rows.
3. row-oriented systems are more efficient when many columns of a single row are required at the same time, and when row-size is relatively small, as the entire row can be retrieved with a single disk seek.
4. row-oriented systems are more efficient when writing a new row if all of the column data is supplied at the same time, as the entire row can be written with a single disk seek.
In practice, row oriented architectures are well-suited for OLTP-like workloads which are more heavily loaded with interactive transactions. Column stores are well-suited for OLAP-like workloads (eg. data warehouses) which typically involve a smaller number of highly complex queries over all data (possibly terabytes).
Storage efficiency vs Random Access
Storage efficiency vs Random Access
Column data is of uniform type; therefore, there are some opportunities for storage size optimizations available in column oriented data that are not available in row oriented data. For example, many popular modern compression schemes, such as LZW, make use of the similarity of adjacent data to compress. While the same techniques may be used on row-oriented data, a typical implementation will achieve less effective results. Further, this behavior becomes more dramatic when a large percentage of adjacent column data is either the same or not-present, such as in a sparse column (similar to a sparse matrix). The opposing tradeoff is Random Access. Retrieving all data from a single row is more efficient when that data is located in a single location, such as in a row-oriented architecture. Further, the greater adjacent compression achieved, the more difficult random-access may become, as data might need to be uncompressed to be read.
Column data is of uniform type; therefore, there are some opportunities for storage size optimizations available in column oriented data that are not available in row oriented data. For example, many popular modern compression schemes, such as LZW, make use of the similarity of adjacent data to compress. While the same techniques may be used on row-oriented data, a typical implementation will achieve less effective results. Further, this behavior becomes more dramatic when a large percentage of adjacent column data is either the same or not-present, such as in a sparse column (similar to a sparse matrix). The opposing tradeoff is Random Access. Retrieving all data from a single row is more efficient when that data is located in a single location, such as in a row-oriented architecture. Further, the greater adjacent compression achieved, the more difficult random-access may become, as data might need to be uncompressed to be read.
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