Hey there, let's tackle these two crucial questions about using S3 as HDFS storage with Spark—these are super common gotchas when switching from HDFS to object stores!

First, let's recap S3's eventual consistency behavior: when your app makes changes (creates, updates, deletes), those changes might not be immediately visible to all reads. While S3 now offers read-after-write consistency for new object puts, updates and deletes still follow eventual consistency—meaning old data might linger in reads for an unpredictable amount of time.

Here's how this hits Spark:

• Pipeline/sequential Job issues: If you run a sequence of Spark Jobs where later Jobs depend on the output of earlier ones (like an ETL pipeline where Job 1 writes data, Job 2 reads and transforms it), you could run into cases where Job 2 doesn't see the latest data from Job 1. For example, if Job 1 updates an existing S3 object, Job 2 might still read the old version right after.
• Iterative workloads: Spark jobs that loop or run multiple iterations (like ML training loops) that rely on updated state in S3 might get stale data, leading to incorrect results.
• Yes, running n Spark Jobs absolutely can lead to data visibility issues: This is especially true if Jobs are chained tightly without any safeguards. Even if you trigger Jobs sequentially, the eventual consistency window could cause the subsequent Job to miss recent changes.

To mitigate this:

• Use idempotent operations: Design your jobs so that re-running them doesn't cause duplicate or incorrect data (e.g., write to new prefixes instead of overwriting existing ones).
• Leverage S3's atomic operations where possible: For example, using rename operations (which are atomic in S3) to swap out old data with new data once the write is complete.
• Avoid tight coupling between Jobs that depend on updates/deletes: If possible, add a small wait window (though this isn't foolproof) or use external checks to verify data visibility before triggering the next Job.

Spark's default partitioning logic for S3 is similar to how it works with HDFS, but with key differences due to S3 being an object store (not a distributed file system with local data nodes):

• Split-based partitioning: Spark splits input data into chunks (called splits) based on the spark.sql.files.maxPartitionBytes configuration (default is 128MB, matching HDFS's default block size). For large S3 objects, Spark will split them into multiple splits, each becoming a Spark partition. Smaller objects will each get their own partition (unless you use coalesce/repartition to combine them).
• No data locality benefit: Unlike HDFS, where Spark can schedule tasks on nodes that hold the data block (for better performance), S3 has no concept of "local" data nodes. So Spark can't leverage data locality here—all tasks will have to pull data over the network from S3, which can add latency.
• Small file problem amplification: If your S3 bucket has lots of small files, Spark will create a huge number of partitions (one per small file). This leads to excessive task scheduling overhead and can severely hurt performance.

To optimize this:

• Combine small files: Run a pre-processing job to merge small S3 objects into larger ones before processing with Spark, or use coalesce/repartition after reading to reduce the number of partitions.
• Tune partition size: Adjust spark.sql.files.maxPartitionBytes based on your workload—if you have network constraints, you might want larger partitions to reduce the number of network calls.

内容的提问来源于stack exchange，提问作者Ged