How does Impala handle situations of data skew?

Impala is a distributed SQL query engine designed for large-scale data processing that can run on Hadoop clusters. Data skew refers to the situation where the amount of data in certain data partitions is significantly higher than in other partitions, leading to a decrease in data processing performance.

Impala can handle data skew by utilizing the following methods:

1. Utilizing partition tables: storing data in partitions based on a specific key field can improve query performance and prevent data skew.
2. Utilizing parallel querying: Impala supports parallel querying, enabling it to handle multiple query tasks simultaneously and reduce query time.
3. Data balancing: The ability to redistribute data evenly across different nodes to prevent data skew.
4. Optimize query performance by adjusting the query plan to avoid performance impacts from data skew.
5. Utilize data compression to decrease storage space for data and increase data processing efficiency.

Overall, Impala can improve data processing efficiency by designing data structures, adjusting data distribution, and optimizing query plans to handle data skew.