In most traditional databases, indexes can greatly improve data access times. However, in a distributed database such as Greenplum, indexes should be used more sparingly. Greenplum Database performs very fast sequential scans; indexes use a random seek pattern to locate records on disk. Greenplum data is distributed across the segments, so each segment scans a smaller portion of the overall data to get the result. With table partitioning, the total data to scan may be even smaller. Because business intelligence (BI) query workloads generally return very large data sets, using indexes is not efficient.
First try your query workload without adding indexes. Indexes are more likely to improve performance for OLTP workloads, where the query is returning a single record or a small subset of data. Indexes can also improve performance on compressed append-optimized tables for queries that return a targeted set of rows, as the optimizer can use an index access method rather than a full table scan when appropriate. For compressed data, an index access method means only the necessary rows are uncompressed.
Greenplum Database automatically creates
PRIMARY KEY constraints for tables with primary keys. To create an index on a partitioned table, create an index on the partitioned table that you created. The index is propagated to all the child tables created by Greenplum Database. Creating an index on a table that is created by Greenplum Database for use by a partitioned table is not supported.
Note that a
UNIQUE CONSTRAINT (such as a
PRIMARY KEY CONSTRAINT) implicitly creates a
UNIQUE INDEX that must include all the columns of the distribution key and any partitioning key. The
UNIQUE CONSTRAINT is enforced across the entire table, including all table partitions (if any).
Indexes add some database overhead — they use storage space and must be maintained when the table is updated. Ensure that the query workload uses the indexes that you create, and check that the indexes you add improve query performance (as compared to a sequential scan of the table). To determine whether indexes are being used, examine the query
EXPLAIN plans. See Query Profiling.
Consider the following points when you create indexes.
WHEREclauses are good candidates for indexes.
CLUSTER command to physically reorder a table based on an index can take a long time with very large tables. To achieve the same results much faster, you can manually reorder the data on disk by creating an intermediate table and loading the data in the desired order. For example:
CREATE TABLE new_table (LIKE old_table) AS SELECT * FROM old_table ORDER BY myixcolumn; DROP old_table; ALTER TABLE new_table RENAME TO old_table; CREATE INDEX myixcolumn_ix ON old_table; VACUUM ANALYZE old_table;
Parent topic: Defining Database Objects
Greenplum Database supports the Postgres index types B-tree and GiST. Hash and GIN indexes are not supported. Each index type uses a different algorithm that is best suited to different types of queries. B-tree indexes fit the most common situations and are the default index type. See Index Types in the PostgreSQL documentation for a description of these types.
Note: Greenplum Database allows unique indexes only if the columns of the index key are the same as (or a superset of) the Greenplum distribution key. Unique indexes are not supported on append-optimized tables. On partitioned tables, a unique index cannot be enforced across all child table partitions of a partitioned table. A unique index is supported only within a partition.
Greenplum Database provides the Bitmap index type. Bitmap indexes are best suited to data warehousing applications and decision support systems with large amounts of data, many ad hoc queries, and few data modification (DML) transactions.
An index provides pointers to the rows in a table that contain a given key value. A regular index stores a list of tuple IDs for each key corresponding to the rows with that key value. Bitmap indexes store a bitmap for each key value. Regular indexes can be several times larger than the data in the table, but bitmap indexes provide the same functionality as a regular index and use a fraction of the size of the indexed data.
Each bit in the bitmap corresponds to a possible tuple ID. If the bit is set, the row with the corresponding tuple ID contains the key value. A mapping function converts the bit position to a tuple ID. Bitmaps are compressed for storage. If the number of distinct key values is small, bitmap indexes are much smaller, compress better, and save considerable space compared with a regular index. The size of a bitmap index is proportional to the number of rows in the table times the number of distinct values in the indexed column.
Bitmap indexes are most effective for queries that contain multiple conditions in the
WHERE clause. Rows that satisfy some, but not all, conditions are filtered out before the table is accessed. This improves response time, often dramatically.
Bitmap indexes are best suited to data warehousing applications where users query the data rather than update it. Bitmap indexes perform best for columns that have between 100 and 100,000 distinct values and when the indexed column is often queried in conjunction with other indexed columns. Columns with fewer than 100 distinct values, such as a gender column with two distinct values (male and female), usually do not benefit much from any type of index. On a column with more than 100,000 distinct values, the performance and space efficiency of a bitmap index decline.
Bitmap indexes can improve query performance for ad hoc queries.
OR conditions in the
WHERE clause of a query can be resolved quickly by performing the corresponding Boolean operations directly on the bitmaps before converting the resulting bitmap to tuple ids. If the resulting number of rows is small, the query can be answered quickly without resorting to a full table scan.
Do not use bitmap indexes for unique columns or columns with high cardinality data, such as customer names or phone numbers. The performance gains and disk space advantages of bitmap indexes start to diminish on columns with 100,000 or more unique values, regardless of the number of rows in the table.
Bitmap indexes are not suitable for OLTP applications with large numbers of concurrent transactions modifying the data.
Use bitmap indexes sparingly. Test and compare query performance with and without an index. Add an index only if query performance improves with indexed columns.
CREATE INDEX command defines an index on a table. A B-tree index is the default index type. For example, to create a B-tree index on the column gender in the table employee:
CREATE INDEX gender_idx ON employee (gender);
To create a bitmap index on the column title in the table films:
CREATE INDEX title_bmp_idx ON films USING bitmap (title);
Greenplum Database indexes do not require maintenance and tuning. You can check which indexes are used by the real-life query workload. Use the
EXPLAIN command to examine index usage for a query.
The query plan shows the steps or plan nodes that the database will take to answer a query and time estimates for each plan node. To examine the use of indexes, look for the following query plan node types in your
You have to experiment to determine the indexes to create. Consider the following points.
ANALYZEafter you create or update an index.
ANALYZEcollects table statistics. The query optimizer uses table statistics to estimate the number of rows returned by a query and to assign realistic costs to each possible query plan.
enable_seqscan) and nested-loop joins (
enable_nestloop), the most basic plans, to force the system to use a different plan. Time your query with and without indexes and use the
EXPLAIN ANALYZEcommand to compare the results.
REINDEX command to rebuild a poorly-performing index.
REINDEX rebuilds an index using the data stored in the index's table, replacing the old copy of the index.
DROP INDEX command removes an index. For example:
DROP INDEX title_idx;
When loading data, it can be faster to drop all indexes, load, then recreate the indexes.