The incorrect description of Hive is that Hive enables low-latency fast query operations on large-scale data sets.

The incorrect description of Hive is from a question in a question paper that had four options, ABCD, and the other correct statements are The best use case for Hive is batch jobs on large datasets; Hive is built on top of static batch-based Hadoop, which typically has high latency and requires a lot of overhead during job submission and scheduling. Hive is built on top of Hadoop, which is based on static batch processing.

The Hive query process adheres strictly to the Hadoop Map Reduce job execution model, where Hive converts the user's HiveQL statement into a Map Reduce job through the interpreter and submits it to the Hadoop cluster.

Scenarios for Hive

Hive is built on top of Hadoop, which is based on static batch processing, and which typically has high latency and requires a significant amount of overhead during job submission and scheduling. As a result, hive is not able to achieve low-latency fast queries on large-scale datasets; for example, hive typically has minute-level time delays in executing queries on datasets of a few hundred megabytes.

So hive is not suitable for applications that require high real-time performance, such as online transaction processing. hive query operation process strictly adheres to the HadoopMapReduce job execution model, hive will be the user's hiveSQL statement through the interpreter to convert to Map Reduce jobs submitted to the Hadoop cluster, Hadoop monitor the job execution process. Hadoop monitors the job execution process and then returns the job execution results to the user.

hive is not designed for online transaction processing, and hive does not provide real-time querying or row-level data update operations. hive is best used for batch processing of large data sets, such as web log analysis.

Reference: Baidu Encyclopedia-hive