Impala Integration Overview
Unravel's Impala tuning tool provides guidance and automation to improve the performance of Impala queries and applications.
Impala brings SQL to Hadoop – The Unravel Impala tuning tool unlocks its full potential.
Enterprise developers and information workers want to be able to use the wealth of SQL skills of their developers on larger data sets. These datasets now reside in Hadoop clusters and data teams use Impala to provide access to these datasets. Unravel’s Impala tuning AI platform gives detailed metrics, troubleshooting insights and recommendations to supercharge your Impala applications and queries.
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Breakthrough insights into Impala SQL operations
Unravel uncovers and understands the factors that lead to Impala application slowdowns and failures.
Unravel automatically detects, diagnoses and resolves slow or failing Impala applications and queries. Unravel for Impala enables you to:
- Identify and resolve Impala resource contention.
- Detect and remediate Impala memory allocation issues.
- Uncover issues stemming from data skew, improper partitioning, and excessive data spilling.
Optimizing Impala resource usage across multi-tenant clusters
Unravel ensures that Impala applications have the resources they need to offer optimal performance.
Unravel ensures that SQL applications that use Impala are performant and production ready for enterprise use. To ensure top query performance, Unravel:
- Identifies Impala runtime conditions such as the job length and delivers metrics for each stage of multi-stage Impala queries.
- Discovers if a table has too many partitions and recommends a different partitioning scheme.
- Quickly detects problematic SQL operators such as poorly constructed joins.
Understanding the operating cost of Impala
Unravel provides detailed reporting, recommendations, insights, dashboards and auto-tuning of Impala applications.
Unravel provides reporting on resource usage and the cost of Impala operations. Unravel provides
- Chargeback reporting for Impala applications with drill down to individual queues, jobs, and queries.
- Quick detection of inefficient Impala applications, with recommendations on configuration changes to improve performance and efficiency.
- Detailed analysis including Gantt charting of multi-stage Impala queries and viewing associated SQL operators and query plans.
