Oracle Database Replication: Methods and Complete Guide

What Is Oracle Replication?

Replication can be defined as the process of copying and maintaining database objects in a distributed multi-database system. With replication, changes applied to one of the databases are captured, stored, and then shared across all the other remote locations.

Oracle replication is an integrated feature of the Oracle server that contains a set of tools to replicate and integrate Oracle with other databases. Oracle replication uses distributed database technology to share data between various sites. With the right Oracle replication tool, you can easily copy Oracle data to other databases, data warehouses, data processing and management platforms.

Oracle database replication can be used in the distribution of datasets across multiple geographic locations, sharing data with stakeholders, enhancing data synchronization between remote and regional offices, eliminating overheads on transactional databases, and many other possibilities.

How Does Oracle Replication Work?

Oracle replication allows users to access information by making data available across multiple locations or systems. Oracle replication creates synchronized copies of the database to be used for different purposes like reporting, testing, and backups. By default, Oracle supports two different forms of replication out of the box: basic and advanced replication.

Basic replication: In basic replication, data replicas provide read-only access to the table data that originates from a primary/master site. Applications can query data from local data replicas to avoid network congestion but must access data at the master sites when updates are needed. Oracle supports basic read-only replication environments by using read-only table snapshots. This type of replication allows for the creation of read-only replicas.

Advanced replication: The Oracle advanced replication features enable applications to update table replicas throughout a replicated database system. This means each replica in the system can provide both read and update access to any table data to ensure transaction consistency and data integrity.

The Oracle replication system functions with the following basic components: replication objects, replication groups, and replication sites. These components are essential for enabling Oracle replication to work effectively.

Replication objects: A replication object can be described as a database object residing in multiple servers in a distributed database system. Any updates made to a replication object on any of the locations where it is found are implemented on all the copies at other sites. Replication objects that can be replicated using Oracle replication tools include tables, views, indexes, triggers, sequences, synonyms, procedures, functions, packages, and package bodies.

Replication groups: A replication group is an aggregation of similar replication objects that are related. Oracle manages replication objects using the replication groups in a replication environment where the objects in the group are handled and managed together. This makes it easy to organize the schema objects for the support of a particular database application.

Replication sites: A replication group can exist at various replication sites. There are two kinds of sites that the replication environment can have, the master and the snapshot sites. A replication site can also be both a master and a snapshot site at the same time. The master site is the control center for managing the replication group and the objects in the group. It maintains a complete copy of all objects in the replication group. Master sites in a multi-master replication environment communicate directly with one another to ensure data and schema changes are synchronized within the replication group. Snapshots at a snapshot site can contain all or a subset of the table data within a master group. They can contain an image or snapshot of a table from a certain point in time. The snapshot is refreshed periodically to synchronize it with the master site.

Why Replicate An Oracle Database?

Oracle has sophisticated mechanisms to manage data concurrently. Replication can be used for a geographically distributed system where the same data has to be available at multiple locations. Replication makes it easier to share, distribute, and create backup and recovery points. Replication is the best way to synchronize data across different locations while maintaining data integrity and consistency.

Some of the common reasons for using Oracle replication include:

Availability: Replication helps improve the availability of data. It provides an alternative data access option if one site becomes unavailable, users can simply query or update data from the other locations where the data is found.
Performance: Replication increases performance as it provides fast access to shared data found on multiple sites. Since the same data is found in various locations, different users can access the servers closest to them thereby reducing the load at all the servers. This incurs lower access costs and invariably improves the performance of the distributed system.
Network Load Redundancy: With replication, data is distributed over multiple regional locations enabling applications to access the nearest regional server instead of one central server, therefore, reducing the network load.
Mass Deployment: With Oracle replication, organizations can deploy many applications as Oracle has a deployment template where multiple snapshot environments can be created quickly using variables to customize each snapshot environment for specific needs.
Disconnected Computing: Snapshots give room for users to work on a subset of databases while not being connected to the central database. This is possible since the snapshot is a complete or partial copy of a master table at a single point in time. The snapshot can be worked on and subsequently, if need be or when the connection is made, users can reconnect with the master to refresh and synchronize the data. The changes made on the snapshots will be automatically updated on the central database and any change that happened on the central database during the disconnection will also be updated on the snapshot.

Four Easy Ways To Replicate An Oracle Database

In this section, we will explore four easy ways to replicate an Oracle database. These methods include full dump and load, incremental approach (table differencing), trigger-based approach, and change data capture. Choosing any of the methods explained below will be dependent on what you want to achieve, the size of the database to be replicated, and the performance of the system.

Full Dump And Load

In this method, a full snapshot, also known as a table dump, is taken at various specified intervals. For each replication interval, the dump can be loaded to another data warehouse or queried. Note that each snapshot taken at an interval becomes a substitute for the previous one. The dump file can be generated using the /*csv*/ annotation with the query, demonstrated in the example below. The command below generates a CSV file which can then be read and loaded into the target database.

SPOOL "/data/emp.csv"
SELECT /*csv*/ * FROM EMP;
SPOOL OFF

Pros & Cons of Full Dump and Load

This approach is very efficient for small tables with probably less than 100 million rows but can be a headache for larger tables. With larger datasets, it takes a longer amount of time to perform the dump which can bog down the system. Also, since the dump is based on a time interval, real-time data replication cannot be implemented.

Incremental Approach (Table Differencing)

The incremental or table differencing approach is carried out when a copy of the source table is compared to an older version of the table to ascertain the difference. This is done periodically to extract the differences in the tables, it gives the latest inserts and updates that are present in the new version of the table providing an accurate view of the changes using native SQL scripts. The example below is an Oracle command to get the difference between two tables named new_version and old_ version, it provides all the inserts and updates in the new version of the table.

SELECT * FROM new_version 
MINUS SELECT * FROM old_version;

The results of this query would allow for the generation of a script that could be run on the target database. This would apply the latest changes to the data in the target system.

Pros & Cons of Incremental Approach

The incremental method provides an accurate view of the latest data in a very simple manner. However, querying the database continuously to get the changes can lead to high computation costs. It is also not very helpful when trying to restore data as you will be required to load a fully backed-up file to get the previous versions of the data. Overall, this approach can become quite complex and create a heavy burden on the source system.

Trigger-Based Approach

This method uses triggers to execute automatically whenever a data change occurs in the database system. Triggers are functions that can be set to fire when a specific event happens within the database. The trigger can be configured to replicate the change from the source table in the Oracle database to another database or simply write the update to a change event table. Triggers in Oracle are a built-in capability that can easily be configured to monitor source tables and perform actions when a change occurs.

Pros & Cons of Trigger-Based Approach

This approach is mostly associated with synchronous replication and solves the problem of real-time ingestion of data. The biggest downside is that it affects the performance of the source database as triggers are known to cause delays in transactions. Like the other methods mentioned above, this method also tends to be tough to scale and apply when dealing with targets outside of the Oracle ecosystem.

Change Data Capture

Change Data Capture (CDC) is a software process that is used to monitor for changes in the data within source tables and apply that data to a target system. CDC is used to keep data in sync by supporting real-time replication across the different source and target systems. Oracle CDC processes replicate Create-Update-Delete (CUD) operations written in SQL using the INSERT, UPDATE, and DELETE commands. CDC makes the replication process effective, easy, and faster as only modifications are copied as opposed to copying the whole table or database for every replication. CDC processes also consistently detect and apply changes in real-time instead of only at specified intervals. CDC is the preferred method of replication for most modern enterprises since it is more flexible, scalable, and consistent than other options. If you’re interested in learning more about Oracle CDC, read our Oracle CDC (Change Data Capture) Everything You Need To Know.

Oracle GoldenGate is one of the available CDC tools that can be used to replicate data from one Oracle database to another. It uses binary log-based replication to facilitate its replication capabilities. The binary logs are historical records of all the changes that happened in the database. These logs contain all the data needed to accurately replicate changes from source to target without actually accessing the database tables directly. GoldenGate can be used in many instances such as multi-cloud ingestion, data lake ingestion, Online Transactional Processing (OLTP) data replication, and other replication use cases. GoldenGate can connect to several target databases through its built-in handlers and makes replication more scalable than the previously mentioned methods.

Despite the prominence and flexibility of Oracle GoldenGate, it is not the ideal solution for all organizations. Many enterprises use GoldenGate to replicate data across a variety of databases like Oracle, SQL Server, and other popular enterprise-grade databases. Most of the GoldenGate installations are either a single-node deployment or a 2-node classic configuration where one is primary and one is secondary. Database replication use cases are, however, no longer limited to only data replication across OLTP databases. Two new use cases have emerged recently that are not a very great fit for GoldenGate.

The first modern use case in data replication is to replicate transactional data into modern analytical cloud platforms. These cloud platforms and the applications using them demand real-time data feeds (via change data capture) from enterprise databases. These modern analytical applications can be classified into three categories:

Real-time business intelligence applications running on real-time cloud platforms like Redshift, Snowflake, Databricks, SingleStore
Machine Learning and AI applications built and deployed on platforms like Databricks Delta Lake.
Operational/Real-time analytics applications running on real-time data platforms like SingleStore, Redis, Imply, and Apache Pinot.

The second modern use case is database modernization. Enterprises are modernizing their database stack and embracing modern open-source technologies like MongoDB, Postgres, and Yugabyte. Zero-downtime migrations from classic enterprise databases like Oracle, DB2, and Microsoft SQL Server to platforms like MongoDB, Postgres, and Yugabyte are increasingly common. Achieving a zero-downtime migration requires CDC-based data replication with first-class support for new and modern open-source platforms.

GoldenGate was built for the previous generation of databases and is not a great solution to address the new use cases mentioned. The functionality needed to accommodate these use cases are extremely relevant for enterprises that want to move their business data to the cloud for storage and analytics. GoldenGate does not have first-class support for modern data platforms like Snowflake or Databricks, though GoldenGate Big Data Adapter mentions Snowflake as a supported target. For such modern data replication use cases, data engineers may want to look for other alternatives.

Pros & Cons of Change Data Capture

CDC is very important in achieving real-time data integration and is efficient in building streaming analytics. It also puts minimal load on the entire network since only changes to rows in the tables are copied from the source data and it does not affect the production database thereby freeing up resources for transactions. The downside of using CDC for replication is the configuration process. Some tools require deep expertise and knowledge to implement. One other potential downfall is that it can be a very slow process to initially load data into the target database. Of course, this is generally par for the course since the initial load generally contains the most amount of data to be written at any given time.

If you’re interested in learning more about the alternatives of Oracle GoldenGate, we wrote a blog that detailed 10 alternative tools.

Using Arcion For Oracle Replication

For many enterprises that are looking to select an Oracle Replication tool that is scalable, reliable, and extremely easy to configure and use, they turn to Arcion. Arcion provides robust data pipelines that offer high availability, leverage log-based CDC, and auto-scalable features. Available with multiple deployment options, Arcion can migrate data to and from on-prem data sources, cloud-based data sources, or a mix of both.

Arcion’s zero-code approach to data replication allows users to easily configure Arcion and build their data pipelines without having to write any code. Arcion can be set up and configured strictly through configuration files or by using Arcion’s intuitive and easy-to-use UI to set up pipelines in a matter of minutes. Arcion also makes implementation smooth by providing extensive documentation, tutorials, blogs, and 24/7 customer support.

Let’s take a look at some specific features that lend themselves well to implementing replication for Oracle databases.

Sub-Second Latency

Many other existing CDC solutions don’t scale for high-volume, high-velocity data, resulting in slow pipelines, and slow delivery to the target systems. Arcion is the only distributed, end-to-end multi-threaded CDC solution that auto-scales vertically & horizontally. Any process that runs on Source & Target is parallelized using patent-pending techniques to achieve maximum throughput. There isn’t a single step within the pipeline that is single-threaded. It means Arcion users get ultra-low latency CDC replication and can always keep up with the forever-increasing data volume on Source.

Oracle Native Log Reader

With our Oracle Native Log Reader feature, users can achieve 10x faster extraction from Oracle but with zero impact on the source system. Arcion now reads directly from Oracle redo log files (online and archive) to stream CDC records out of an Oracle database. This is a tremendous win for all our users. With Oracle Native Log Reader, Arcion users can stream changes out of Oracle without connecting to Oracle and running expensive LogMiner queries inside Oracle (which could impact the performance of the Oracle server and also consume a lot of PGA memory).

Another benefit of the Native Log Reader compared to Oracle API (e.g., LogMiner) is the complete decoupling from an existing Oracle feature which had several limitations (e.g. LogMiner cannot mine binary column data more than 4KB).

But the secret sauce is when the Oracle Native Log Reader is combined with our scalable, parallel, end-to-end multi-threaded architecture. Arcion can extract CDC records out of Oracle faster than any other traditional CDC solution. If you ever have trouble extracting data out of high-volume Oracle installations, like Oracle RAC, because of the terabytes of redo log it produces daily, Arcion is your answer.

Automatic DDL/Schema Evolution

Keeping schemas aligned between Source and Target is a classic problem among data engineers who set up and maintain pipelines. Previously, replication would need to be stopped if there was a change to the DDL or schema on the source database. Relevant updates would need to be made to the config files or schema to reflect the change on the target system. Only then could replication resume or, worse yet, be restarted. This, of course, leads to downtime, consumes expensive compute resources, and can be prone to user error and data loss when editing configuration or schema files.

Arcion supports automated DDL and schema evolution without requiring any user intervention, including being the only platform to currently support out-of-the-box DDL replication with Snowflake and Databricks. Arcion automatically replicates DDL and schema updates to the target database when a change is made on the source, eliminating downtime. Furthermore, Arcion does this automatically, meaning no manual changes to the configuration files and no manual pipeline restarts are needed. Everything is taken care of by the Arcion platform without requiring any user intervention.

100% Agentless Change Data Capture

Arcion is the only CDC vendor in the market that offers 100% agentless CDC to all its supported 20+ connectors. Arcion’s agentless CDC connectors apply to all the complex enterprise databases modern enterprises use, like Microsoft SQL Server, MongoDB, and all versions of Oracle. Arcion reads directly from the transaction logs, never reading from the database itself. Previously, data teams faced administrative nightmares and security risks associated with running agent-based software in production environments. You can now replicate data in real-time, at scale, with guaranteed delivery — but without the inherent performance issues or the security concerns of agent-based connectors.

Data Consistency Guaranteed

Arcion provides transactional integrity and data consistency through its CDC technology. To further this effort, Arcion also has built-in data validation support that works automatically and efficiently to ensure data integrity is always maintained. It offers a solution for both scalable data migration and replication while making sure that zero data loss has occurred.

20+ Pre-Built Enterprise Data Connectors

Arcion has a library of pre-built data connectors. These connectors can provide support for over 20 enterprise databases, data warehouses, and cloud-native analytics platforms (see full list). Unlike other CDC tools, Arcion provides full control over data while still maintaining a high degree of automation. Data can be moved from one source to multiple targets or multiple sources to a single target depending on your use case. This means that if you branch out into other technologies outside of Oracle, you’ll already have the capability within Arcion to handle your new sources and targets without the need for another pipeline technology.

Here are a few videos to show you how Arcion replicates data from Oracle to other target database and cloud analytic platform:

Oracle To MongoDB with Arcion Cloud

Oracle To MongoDB with Arcion Self-hosted

Oracle to MongoDB with Denormalization with Arcion Self-hosted

Live ingest 10M rows of TPCH schema from Oracle to SingleStore and perform large transactions on Oracle (10k+ row updates) using log-based CDC

Oracle to Databricks using Change Data Capture with Arcion & Databricks Partner Connect

Oracle to Snowflake with Arcion Cloud

Upgrade Oracle 12c to Oracle 19c with Arcion Self-hosted

Conclusion

This write-up explained what Oracle replication is all about and offered you methods to replicate your data. We first defined what data replication is, before going further to explore Oracle replication techniques. We took a deep dive into native Oracle replication techniques and a few others that may be useful. The techniques we covered included full dump and load, incremental approach (table differencing), trigger-based approach, and change data capture.

Lastly, we took a deep dive into using Arcion for Oracle CDC. We looked at many of the advantages of Arcion over some of the previously mentioned techniques, including the advantages of using Arcion instead of Oracle GoldenGate. To get started with Oracle replication and try out Arcion for yourself, simply download Arcion Self-hosted for free or spin up pipelines in Arcion Cloud in minutes (no payment info required.).

What is Oracle Replication? Methods and Guide