Unloading data from an extensive database can be a long and windy process. Extracting data from an Oracle database with millions of rows is different than working with a few rows of data in an online transaction processing application (OLTP). Fortunately, some techniques optimize the process and avoid frustrations and errors during an oracle database extraction. Here are some tips to improve the performance of your extract-transform-load (ETL) jobs.
1. Extract the Data On the Server
Run your data extraction as close to the source as possible. That means you should unload the data onto the server instead of a network client.
Admittedly, there are situations where unloading data onto the server may not be possible. In such cases, you need to use a program or tool that permits the use of large array sizes. This will reduce the number of trips between the client and server and reduce the load on the network infrastructure.
You may also unload the data to a flat file on the server. Then, you can move the data from the file in batches through the network.
2. Use an SSD Hard Drive
A solid-state drive (SSD) reads about ten times faster and can write data 20 times faster than the conventional hard drive. So one of the ways to boost your oracle DB extraction efficiency is to unload the data unto a solid-state drive.
If you have direct access to the old database server, you can attach a solid-state drive to it. Or you may use a solid-state drive on your network client.
In addition to using an SSD, you may also design your data extraction to work with parallel processes. That means the work will be divided among two processor cores for faster reading and writing.
3. Automate Extraction With the Right Tool
Many tools are now available for data extraction from Oracle and other popular enterprise databases. These tools speed up your data unloading with a variety of techniques and technologies.
If you want to choose a tool for your data extraction, here are some of the features to look for:
- Parallel query execution that allows you to extract millions of rows in minutes
- Customization through simple scripts or a GUI.
- Ability to produce flat files that all applications can use.
- Capacity to work with multiple databases.
4. Minimize Nested Loops
Nested loops work well for OLTP applications. When a few rows from a large table need to join another table, nested loops work well with index scans.
However, an extraction job that performs a read operation on thousands or millions of rows will not be efficient with a nested loop. Instead, use a hash join.
You can check your query optimizer in advance to know if your query will require a nested loop join and tweak the query to remove it.
5. Leave Functions Out of Your WHERE Conditions
Putting function calls in your WHERE clause or condition may seem harmless. Your query optimizer may not estimate the time for the query accurately.
Avoid placing string manipulation functions like Substr, Upper, or To_char in your WHERE clause. If you can’t avoid such string transformations, you can perform the transformation and create a temporary table first. Then, extract the rows of the temp table without using any WHERE or filtering conditions.
Applying these tips will help you to plan and execute a more efficient Oracle ETL job. However, not all data extraction jobs can be handled casually, especially in the healthcare industry. For such jobs, you may need to work with a vendor that specializes in healthcare data migration.
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