About Release 8.0.0
This site contains documentation for HPE Data Fabric release 8.0.0, including installation, configuration, administration, and reference content, as well as content for the associated ecosystem components and drivers.
8.0.0 Installation
This section contains information about installing HPE Data Fabric software. It also contains information about how to migrate data and applications from an Apache Hadoop cluster to a HPE Ezmeral Data Fabric cluster.
8.0.0 Upgrade
This section describes how to upgrade HPE Data Fabric software.
8.0.0 Data Fabric
HPE Data Fabric is the industry-leading data platform for AI and analytics that solves enterprise business needs.
- HPE Data Fabric File Store
  HPE Data Fabric File Store is a distributed file system for data storage, data management, and data protection. File Store supports mounting and cluster access via NFS and FUSE-based POSIX clients (basic, platinum, or PACC) and also supports access and management via HDFS APIs.
- HPE Data Fabric Object Store
  The HPE Data Fabric Object Store is a native object storage solution that efficiently stores objects and metadata for optimized access.
- HPE Data Fabric Database
  HPE Data Fabric Database is an enterprise-grade, high-performance, NoSQL database management system that you can use for real-time, operational analytics.
  - Architecture
    HPE Data Fabric Database is an enterprise-grade, high performance, NoSQL (“Not Only SQL”) database management system. You can use it to add realtime, operational analytics capabilities to big data applications. As a multi-model NoSQL database, it supports both JSON document models and key-value data models.
  - Data Models
    HPE Data Fabric Database can be used as both a document database and a column-oriented database. As a document database, JSON documents are stored in HPE Data Fabric Database JSON table. As a column-oriented database, binary files are in stored HPE Data Fabric Database binary tables.
  - Secondary Indexes
    Beginning with release version 6.0, HPE Data Fabric Database JSON natively supports secondary indexes on fields in JSON tables. Indexes provide you with flexible, high performance access to data stored in HPE Data Fabric Database.
    - Secondary Index Concepts
      Describes secondary index concepts, including use cases, types of indexes, types of queries that benefit from indexes, and how indexes are implemented.
      - Uses for Secondary Indexes
        Describes typical use cases that can benefit from secondary indexes.
      - Types of Secondary Indexes
        HPE Data Fabric Database JSON supports several index types, including simple indexes, composite indexes, hashed indexes, and indexes with casting. This section describes the properties of these indexes and the situations where each provides value.
      - Data Types and Secondary Index Fields
        Secondary indexes support a specific set of data types. This section describes how indexed and included fields in secondary indexes behave for various categories of data types.
      - Restrictions on Secondary Indexes
        This topic lists and describes the restrictions on secondary indexes. It is important for you to understand the type, size, field definition, option, and index use restrictions when defining and using secondary indexes.
      - Queries that Benefit from Secondary Indexes
        Secondary indexes benefit queries with filter conditions, ORDER BY clause, and projections.
        Using Indexes to Optimize Equality Conditions
        Using indexes can help you improve the performance of queries that have equality conditions. You can define indexes that optimize equality conditions on scalar data fields, nested document and array fields, and container field paths.
        Using Indexes to Optimize Range Conditions
        Indexes can improve the performance of queries that have range conditions. The range conditions can appear in combination with equality conditions when the most significant index keys have equality conditions. You can define indexes that optimize range conditions on scalar data fields and container field paths.
        Using Indexes to Optimize ORDER BY Queries
        Using indexes can help you improve the performance of queries that have an ORDER BY clause. This includes ORDER BY clauses with either ascending or descending sorts, as well as more than one ordering field. The same index can optimize both filter conditions and the ORDER BY clause.
        Using Indexes to Optimize Projections in Queries
        OJAI queries that do not use the OJAI Distributed Query Service can use indexes even when there are no filter conditions referencing the fields of an index. This requires a full scan of the index. However, in cases where all fields referenced in the query are fields in an index, the need to read the HPE Data Fabric Database JSON table is eliminated. The referenced fields can be either indexed fields or included fields
        Using Multiple Indexes to Optimize Query Conditions
        Indexes benefit queries that have multiple filter conditions. The OJAI Distributed Query Service can optimize these queries by creating query plans that scan multiple indexes and take the intersection of the matching documents.
      - Selection and Execution of Secondary Indexes
        This section provides an overview of secondary index selection and execution in HPE Data Fabric Database JSON. It describes the variations in functionality, depending on the components you are using.
      - Implementation of Secondary Indexes
        This topic describes how HPE Data Fabric Database implements secondary indexes. It provides an overview of basic architectural concepts and the rationale behind design choices.
    - Understanding the Secondary Index Workflow
      Describes the overall workflow for using secondary indexes. This includes the roles of different users and the workflow steps involved.
    - Designing Secondary Indexes
      It is important that you create secondary indexes that provide the most benefit to your HPE Data Fabric Database JSON queries. This topic describes a general design approach that includes identifying query patterns, using common query patterns involving filters and ordering to determine which indexes to create, weighing the benefits of indexes against their update and storage costs, and taking into consideration index limitations.
  - Change Data Capture
    The Change Data Capture (CDC) system allows you to capture changes made to data records in HPE Data Fabric Database tables (JSON or binary) and propagate them to a HPE Data Fabric Streams topic.
  - Table Replication
    You can replicate data in one table to another table that is in the same cluster or in a separate cluster. This type of replication is in addition to the automatic replication that occurs with table regions within a volume.
  - Gateways for Indexing HPE Data Fabric Database Data in Elasticsearch
    As of release version 6.0, HPE Data Fabric Database Elastic Search integration capability is deprecated and no longer available in the HPE Data Fabric Database product.
- HPE Data Fabric Streams
  HPE Data Fabric Streams brings integrated publish and subscribe messaging to the HPE Data Fabric.
- HPE Ezmeral Unified Analytics
  Describes the HPE Ezmeral Unified Analytics Software and provides a link to more information.
- Kubernetes Interfaces for Data Fabric
  This section describes the Kubernetes Interfaces for Data Fabric, which include the Container Storage Interface (CSI) driver for multiple container-orchestration systems, and the FlexVolume driver for Kubernetes.
- Cluster Management
  Provides a synopsis of the various cluster components and their management.
- Performance
  Describes how to tune system performance, manage RDMA, and optimize CLDB tables.
- Security
  Provides an overview of the Data Fabric security features.
- YARN
- Client Connections
  The following sections describe how a client connects to local and remote Data Fabric clusters.
8.0.0 Administration
This section describes how to manage the nodes and services that make up a cluster.
8.0.0 Development
This section contains information related to application development for Ezmeral ecosystem components and HPE Data Fabric products, including the file system, Database (Key-Value and JSON), and Event Streams.
Other Docs
This section contains release-independent information, including: Installer documentation, Ecosystem release notes, interoperability matrices, security vulnerabilities, and links to other Data Fabric version documentation.
Glossary
Definitions for commonly used terms in MapR Converged Data Platform environments.

Queries that Benefit from Secondary Indexes

Secondary indexes benefit queries with filter conditions, ORDER BY clause, and projections.

They benefit these query elements in the following ways:

Filter Conditions: Eliminates full table scans, reducing the number of documents that HPE Data Fabric Database reads, if the filtering fields are keys in an index.

ORDER BY Clause: Eliminates the need to sort the data after scanning the index, if the index's sort order matches the query's sort order.

Projections: Eliminates the need to read the HPE Data Fabric Database JSON table, if all fields referenced in the query are fields in an index.

NOTE

The projections optimization is not supported for OJAI queries that execute through the OJAI Distributed Query Service, and SQL queries issued to Drill. See OJAI Distributed Query Service for details about the types of OJAI queries that use the service.

The following topics describe the specific query types and provide examples.

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HPE Data Fabric 8.0.0 Software Documentation
Abstract	This site contains documentation for HPE Data Fabric Software version 8.0.0 including installation, configuration, administration, and reference content, as well as content for the associated bundled ecosystem components and drivers.
Published	November 2025
Edition	8.0.0
Topic last updated	2020-08-04