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.

indexer_filter_splits

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).

index-decision

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 on date 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 assuming salaries table is sorted on salary, 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 on phone and a Bitmap Index on type will help.

Adding your own Index Type

See Adding your own Index Type.

Access Control

See Built-in System Access Control.