Problem

While performing COUNT operations on a DataFrame or temporary view created from a Delta table in Apache Spark, you notice the COUNT operation intermittently returns zero or an incorrect number of records, even when the data exist.  

Cause

You have parallel DELETE or UPDATE operations interfering with the COUNT operation performed on the temporary view or cached DataFrame, leading to temporary record loss or outdated statistics. 

Context 

When DELETE tasks run simultaneously with COUNT queries, they modify the underlying data, which can result in the COUNT operation observing empty tables or outdated statistics. Empty tables and outdated statistics occur because Spark’s execution triggers a local scan and recompute the DataFrame, invalidating cached states due to the detected changes in the source table. 

Using the same compute cluster for both queries and data modifications further exacerbates inconsistent data count, leading to data volatility and inconsistencies in the results during real-time modifications of the table.

Solution

• Schedule DELETE or UPDATE operations and COUNT queries to run sequentially instead of in parallel. This prevents temporary inconsistencies in the table and ensures accurate results.
• Save the DataFrame to a checkpoint or temporary table in a physical location or file. This creates a stable snapshot of the data that is immune to concurrent operations and reduces the risk of data loss in case of failures.
• Use snapshot isolation to provide a consistent view of the data throughout the duration of your operations. For example, you can query a specific version of the table. 

df = spark.sql("SELECT * FROM <your-catalog>.<your-schema>.<your-table> VERSION AS OF 1")
df.createOrReplaceTempView("<temp-view-name>")