Apache Hive was initially released in 2010 to provide a SQL interface on top of raw data files. However, collecting data is just the first step. These object stores provide a simple way of storing and retrieving vast amounts of information at a relatively low price point, and with little operational overhead. ![]() This is aided by the rise of distributed file stores such as Hadoop Distributed File System (HDFS), as well as cloud storage solutions like AWS Simple Storage Service (S3), Azure Blob, and Google Cloud Storage (GCS). Statista's research department estimates that the global data creation will reach more than 180 zettabytes by 2025. Companies, both large and small, collect an ever-growing number of data points. Why it's so interesting? Because Apache Iceberg can somehow manage this to not overwrite the data files but only the metadata.Īpache Iceberg comes with a set of logical rules and one of them is RewriteDelete.The amount of data produced today is staggering. Since there is no 1-1 relationship between the high-level and low-level writers, I prepared a summary here: Metadata deleteĪpart from the writing classes, another point from the documentation that spotted my attention was the DELETE operation, and more precisely, its version working on the whole partitions. It's used when the table format is set to ORC. This rolling writer creates a new data file whenever the number of buffered records reaches the targeted threshold from write.target-file-size-bytes. It's also used as a physical writing layer in the FanoutDataWriter. This one is used for all data formats but ORC when the table is not partitioned. On the other hand, it's more demanding in CPU since it requires the data to be sorted before writing. This writer is more optimal than the FanoutDataWriter in terms of memory usage since it only keeps the writer for one partition open at a time. When the fanout remains disabled, Apache Iceberg partitioning writer uses the ClusteredDataWriter instance. Its usage is controled by the flag explained before. It's a specialization of the PartitionedDataWriter. Instead, they act as wrappers adding an optional partitioning logic. This task is delegated to a specialized data writer. Both don't perform the physical row writing, though. They're high-level data writer abstractions used when the partitioning information is, respectively, missing and present.
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