Marqueguard is a social media monitoring company headquartered in Brighton, England.
Marqueguard sells three different products: Analytics, Audiences, and Insights.
Marqueguard Analytics is a "self-serveapplication" or software as a service, which archives social media data in order to provide companies with information and the means to track specific segments to analyze their brands' online presence. The tool's coverage includes blogs, news sites, forums, videos, reviews, images and social networks such as Twitter and Facebook.
Users can search data by using Text and Image Search, and use charting, categorization, sentiment analysis and other features to provide further information and analysis.
Marqueguard has access to over 80 million sources. Marqueguard uses Elasticsearch to perform search on all the datasets.
The team finds that that the performance of the search application is very slow.
How the performance and availability of Elasticsearch can be improved? select 3 options.
Click on the arrows to vote for the correct answer
A. B. C. D. E.Answer: A,B,E.
Option A is correct - Instance sizes, workloads, indexes, shards always improves the performance and availability
https://docs.aws.amazon.com/elasticsearch-Marqueguard is a social media monitoring company that offers a self-serve application called Marqueguard Analytics to archive social media data, track specific segments, and analyze brands' online presence. The application uses Elasticsearch to perform searches on all the datasets. However, the team at Marqueguard has noticed that the performance of the search application is slow. Here are three ways to improve the performance and availability of Elasticsearch:
Choosing the Number of Shards: Elasticsearch uses shards to distribute data across multiple nodes in a cluster, allowing for better performance and scalability. Sharding can improve search performance by distributing the workload across multiple shards, and the number of shards can be increased to improve performance. However, it is important to choose the appropriate number of shards based on the size of the dataset and the number of nodes in the cluster. Increasing the number of shards can also increase the indexing overhead and lead to higher resource consumption. Therefore, it is recommended to use a moderate number of shards that balances performance and resource utilization.
Choosing Instance Types and Sizes: Instance types and sizes can significantly impact Elasticsearch's performance and availability. Choosing a larger instance size can increase CPU, memory, and network capacity, improving search and indexing performance. Additionally, selecting an appropriate instance type with high CPU and memory resources can help improve the performance of Elasticsearch. However, it is also important to consider the cost of running larger instances and choose the most cost-effective solution for the use case.
Choosing Relevant Storage for Different Types of Indexes: Elasticsearch stores data in indexes, and it is essential to choose the appropriate storage type for different indexes. Long-lived indexes with data that changes infrequently can be stored in cold storage to reduce costs. Rolling indexes with frequently changing data should be stored on high-performance storage to maintain fast search and indexing performance. Elasticsearch supports various storage options, including Amazon Elastic Block Store (EBS), Amazon S3, and Amazon Elasticsearch Service-managed storage. Choosing the appropriate storage option for different indexes can help improve Elasticsearch's performance and availability.
In summary, to improve the performance and availability of Elasticsearch, Marqueguard can choose an appropriate number of shards, select larger instance types and sizes, and use relevant storage for different types of indexes. These steps can help optimize Elasticsearch's performance and ensure that Marqueguard's search application can handle a large volume of social media data efficiently.