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With Z-order and Hilbert curves, the effectiveness of clustering decreases with each column added - so you'd want to zorder only the columns that you's actually use so that it's speed up your workloads.

coalesce avoids a full shuffle and could be used to decrease the number of partitionsrepartition results in a full shuffle and could be used to increase or decrease the number of partitions

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import sqlContext.implicits._import org.apache.spark.sql.functions._inputDF.select(   unix_timestamp($"unix_timestamp").alias("unix_timestamp"),   from_utc_timestamp($"unix_timestamp".cast(DataTypes.TimestampType), "UTC").alias("UTC"),   from_utc_tim...

While Spark does use a micro-batch execution model, this does not have much impact on applications, because the batches can be as short as 0.5 seconds. In most applications of streaming big data, the analytics is done over a larger window (say 10 min...

I got this question from some customers and I want ti clarify here tooI think we are conflating two things:Catalyst optimizer is about coming up "Steps to take to execute the query". For example, the optimizer will decide how and when to do the join...