Using Parallel Indexing to Reduce Runtimes in Azure Blob Container
Question
You have documents stored in an Azure blob container.
With growing data volumes, your processing times increase.
To address such a computational intensive indexing process and reduce the runtimes, you decide to use parallel indexing in your AI enrichment pipeline.
Review the steps given below and sequence them in the correct order of execution: Step 1: Indexers are created corresponding to each data source. Step 2: Per container or folder, several data sources are set up. Step 3: Use multiple search units to schedule a number of indexers at the same time. Step 4: All indexers point to the same target search index for writing. Step 5: The data is partitioned into multiple folders or containers.
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: B.
Option A is incorrect because the first step is to partition into multiple folders or containers.
Option B is correct.
Here is the correct order of the sequence.
Step 1: The data is partitioned into multiple folders.
Step 2: Per container or folder, several data sources are set up.
Step 3: Indexers are created corresponding to each data source.
Step 4: All indexers point to the same target search index for writing.
Step 5: Use multiple search units to schedule a number of indexers at the same time.
Option C is incorrect because the first step is to partition into multiple folders or containers.
Option D is incorrect because data sources need to be set up before creating indexers.
Reference:
To learn more about indexing large datasets in blob containers, use the link given below:
The correct sequence of execution steps for using parallel indexing in an AI enrichment pipeline with Azure blob container is as follows:
C. Step 2 -> Step 4 -> Step 5 -> Step 1 -> Step 3
Here's a detailed explanation of each step:
Step 2: Per container or folder, several data sources are set up. In this step, you need to set up your data sources. You may have multiple files in a container or folder that needs to be indexed. You can create multiple data sources for each file or group of files that need to be indexed together.
Step 4: All indexers point to the same target search index for writing. In this step, you need to ensure that all the indexers you create point to the same target search index for writing. This ensures that all the data is stored in a single index, making it easy to query and analyze.
Step 5: The data is partitioned into multiple folders or containers. In this step, you need to partition your data into multiple folders or containers. This helps you to distribute the load across different machines and process data in parallel.
Step 1: Indexers are created corresponding to each data source. In this step, you need to create an indexer corresponding to each data source. This will help to extract data from the data sources and write it to the target search index.
Step 3: Use multiple search units to schedule a number of indexers at the same time. In this step, you need to use multiple search units to schedule a number of indexers at the same time. This helps you to speed up the indexing process and reduce the overall processing time.
Therefore, the correct sequence of execution steps for using parallel indexing in an AI enrichment pipeline with Azure blob container is Step 2 -> Step 4 -> Step 5 -> Step 1 -> Step 3.
