openLooKeng Heuristic Index
Introduction
Indexes can be created using one or more columns of a database table, providing faster random lookups. Most Big Data formats such as ORC, Parquet and CarbonData already have indices embedded in them.
The Heuristic Indexer allows creating indexes on existing data but stores the index external to the original data source. This provides several benefits:
- The index is agnostic to the underlying data source and can be used by any query engine
- Existing data can be indexed without having to rewrite the existing data files
- New index types not supported by the underlying data source can be created
- Index data does not use the storage space of the data source
Use cases
Currently, Heuristic Index is only supports the Hive connector with tables using ORC storage format.
1. Filtering scheduled Splits during query execution
Index types supported: Bloom Index, Btree Index, MinMax Index
When the engine needs to read data from a data source it schedules Splits. However, not all Splits will return data if a predicate is applied.
For example: select * from test_base where j1='070299439'
By keeping an external index for the predicate column, the Heuristic Indexer can determine whether each split contains the values being searched for and only schedule the read operation for the splits which possibly contain the value.
2. Filtering Stripes when reading ORC files
Index types supported: Bloom Index, MinMax Index
Similar to Split filtering above, when using the Hive connector to read ORC tables, Stripes can be filtered out based on the specified predicate. This reduces the amount of data read and improves query performance.
3. Filtering rows when reading ORC files
Index types supported: Bitmap Index
Going one level lower, once the rows are read, they must be filtered if a predicate is present. This involves reading rows and then using the Filter operator to discard rows that do not match the predicate.
By creating a Bitmap Index for the predicate column, the Heuristic Indexer will only read rows which match the predicate, before the Filter operator is even applied. This can reduce memory and cpu usage and result in improved query performance, especially at higher concurrency.
Getting started
This section gives a short tutorial which introduces the basic usage of Heuristic Index through a sample query. For a complete list of configuration properties see Properties.
1. Configure indexer settings
In etc/config.properties
, add these lines:
hetu.heuristicindex.filter.enabled=true
hetu.heuristicindex.filter.cache.max-memory=10GB
hetu.heuristicindex.indexstore.uri=/opt/hetu/indices
hetu.heuristicindex.indexstore.filesystem.profile=index-store-profile
Path whitelist:["/tmp", "/opt/hetu", "/opt/openlookeng", "/etc/hetu", "/etc/openlookeng", current workspace]
Note:
LOCAL
filesystem type should only be used in testing or single node clusters.HDFS
filesystem type should be used in production in order for the index to be accessible by all nodes in the cluster.- All nodes should be configured to use the same filesystem profile.
- Heuristic Index can be disabled while the engine is running by setting:
set session heuristicindex_filter_enabled=false;
Filesystem profile file should be placed at etc/filesystem/index-store-profile.properties
,
where index-store-profile
is the name referenced above in config.properties
:
fs.client.type=hdfs
hdfs.config.resources=/path/to/core-site.xml,/path/to/hdfs-site.xml
hdfs.authentication.type=NONE
fs.hdfs.impl.disable.cache=true
If the kerberos authentication is enabled, additional configs hdfs.krb5.conf.path
,
hdfs.krb5.keytab.path
, and hdfs.krb5.principal
must be provided.
With Heuristic Indexer configured, start the engine.
2. Identify a column to create index on
For a query like:
SELECT * FROM hive.schema.table1 WHERE id="abcd1234";
Where id
is unique, a Bloom Index on can significantly decrease the splits to read
when scanning table1.
3. Create index
To create index run the following statement:
CREATE INDEX index_name USING bloom ON table1 (column);
Note: Index for decimal type columns is not supported currently. For more details follow the issue
4. Run query
After index is created, run the query; index will start loading in the background. Subsequent queries will utilize the index to reduce the amount of data read and query performance will be improved.
Index Statements
See Heuristic Index Statements.
Supported Index Types
Index ID | Filtering type | Best Column type | Supported query operators | Notes | Example |
---|---|---|---|---|---|
Bloom | Split Stripe | High cardinality (such as an ID column) | = IN | create index idx using bloom on hive.hindex.users (id); select name from hive.hindex.users where id=123 | |
Btree | Split | High cardinality (such as an ID column) | = > >= < <= IN BETWEEN | Table must be partitioned | create index idx using btree on hive.hindex.users (id) where regionkey IN (1,4) with ("level"='partition') (assuming table is partitioned on regionkey) select name from hive.hindex.users where id>123 |
MinMax | Split Stripe | Column which table is sorted on | = > >= < <= | create index idx using bloom on hive.hindex.users (age); (assuming users is sorted by age) select name from hive.hindex.users where age>25 | |
Bitmap | Row | Low cardinality (such as Gender column) | = IN | create index idx using bitmap on hive.hindex.users (gender); select name from hive.hindex.users where gender='female' |
Note: unsupported operators will still function correctly but will not benefit from the index.
Choosing Index Type
The Heuristic Indexer helps with queries where data is being filtered by a predicate. Identify the column on which data is being filtered and use the decision flowchart to help decide what type of index will work best.
Cardinality means the number of distinct values in the column relative to
number of total rows. For example, an ID
column has a high cardinality
because IDs are unique. Whereas employeeType
will have low cardinality
because there are likely only a few different types (e.g. Manager, Developer,
Tester).
Example queries:
SELECT id FROM employees WHERE site = 'lab';
In this query
site
has a low cardinality (i.e. not many sites) so Bitmap Index will help.SELECT * FROM visited WHERE id = '34857' AND date < '2020-01-01';
In this query
id
has a high cardinality (i.e. IDs are likely unique) and table is partitioned ondate
so Btree Index will help.SELECT * FROM salaries WHERE salary > 50251.40;
In this query
salary
has a high cardinality (i.e. salary of employees will slightly vary) and assumingsalaries
table is sorted onsalary
, MinMax Index will help.SELECT * FROM assets WHERE id = 50;
In this query
id
has a high cardinality (i.e. IDs are likely unique) but the table is not partitioned, so Bloom Index will help.SELECT * FROM phoneRecords WHERE phone='1234567890' and type = 'outgoing' and date > '2020-01-01';
In this query
phone
has a high cardinality (i.e. there are many phone numbers, even if they made multiple calls),type
has low cardinality (only outgoing or incoming), and the data is partitioned on date. Creating a Btree Index onphone
and a Bitmap Index ontype
will help.
Adding your own Index Type
See Adding your own Index Type.