Hey there! Let's break down your questions about Impala's differences from Hive clearly—these are great points to clarify:

How does Impala avoid using MapReduce compared to Hive? #

Instead of leaning on Hive's MapReduce framework for query execution, Impala uses a custom distributed query engine built to mimic the architecture of parallel relational databases. This engine cuts out MapReduce entirely by directly accessing data stored in HDFS, HBase, or other supported storage systems. No spinning up MapReduce jobs, no waiting for batch processing overhead, no serializing/deserializing data between stages—just direct, targeted access to your data.

Why does Impala achieve lower latency than Hive? #

The lower latency comes down to a few key architectural choices:

• No MapReduce overhead: MapReduce has inherent delays from job startup, intermediate data writes to disk, and scheduling. Impala skips all that, so queries get up and running faster.
• Optimized direct data access: Impala uses its own I/O layer to read data directly from storage, tuned for low-latency interactive queries rather than Hive's batch-focused model.
• Smart query planning: Impala's query planner and optimizer generate efficient execution plans tailored to your query and data layout (like partitioning or bucketing). It even uses runtime code generation to speed up execution on the fly.
• Proven performance: Depending on the query type and cluster setup, Impala can be an order of magnitude faster than Hive for many workloads.

How does Impala retrieve data without relying on MapReduce? #

Impala doesn't need MapReduce to fetch data thanks to its own set of dedicated daemons that handle data retrieval directly:

• impalad daemons run on cluster nodes and read data blocks straight from storage (like HDFS) using optimized readers.
• It takes advantage of Hadoop's data locality to process data on the nodes where it's stored, cutting down on network data transfer.
• Impala supports columnar formats like Parquet and ORC (plus text files), and uses columnar storage optimizations to only read the columns your query actually needs—this drastically reduces unnecessary I/O.

内容的提问来源于stack exchange，提问作者vijayinani