Minimum time in milliseconds to wait before retrying a status-check Number of times to check whether a commit succeeded after a connection is lost before failing due to an unknown commit state Total retry timeout period in milliseconds for a commit Maximum time in milliseconds to wait before retrying a commit Minimum time in milliseconds to wait before retrying a commit Number of times to retry a commit before failing Isolation level for merge commands: serializable or snapshot Mode used for merge commands: copy-on-write or merge-on-read (v2 only) Isolation level for update commands: serializable or snapshot Mode used for update commands: copy-on-write or merge-on-read (v2 only) Isolation level for delete commands: serializable or snapshot Mode used for delete commands: copy-on-write or merge-on-read (v2 only) The max number of previous version metadata files to keep before deleting after commitĮnables the fanout writer in Spark that does not require data to be clustered uses more memoryĮnables the object storage location provider that adds a hash component to file paths Ĭontrols whether to delete the oldest tracked version metadata files after commit Includes partition-level summary stats in snapshot summaries if the changed partition count is less than this limit Metrics mode for column ‘col1’ to allow per-column tuning none, counts, truncate(length), or fullĬontrols the size of files generated to target about this many bytesĬontrols the size of delete files generated to target about this many bytesĭefines distribution of write data: none: don’t shuffle rows hash: hash distribute by partition key range: range distribute by partition key or sort key if table has an SortOrderĭefines distribution of write delete dataĭefines distribution of write update data inferred-column-defaultsĭefines the maximum number of columns for which metrics are collectedĭefault metrics mode for all columns in the table none, counts, truncate(length), or full Optional custom implementation for LocationProvider ORC compression strategy: speed, compressionĬomma separated list of column names for which a Bloom filter must be createdįalse positive probability for Bloom filter (must > 0.0 and < 1.0) ORC compression codec: zstd, lz4, lzo, zlib, snappy, none The maximum number of bytes for a bloom filter bitsetĪvro compression codec: gzip(deflate with 9 level), zstd, snappy, uncompressedĭefine the default ORC stripe size, in bytesĭefine the default file system block size for ORC files Hint to parquet to write a bloom filter for the column: col1 Parquet compression codec: zstd, brotli, lz4, gzip, snappy, uncompressed The batch size for parquet vectorized readsĬontrols whether orc vectorized reads are usedĭefault file format for the table parquet, avro, or orcĭefault delete file format for the table parquet, avro, or orc The estimated cost to open a file, used as a minimum weight when combining splits.Ĭontrols whether Parquet vectorized reads are used Number of bins to consider when combining input splits Target size when combining metadata input splits Target size when combining data input splits (“year”,”month”).format(“orc”).option(“compression”, “snappy”).mode(“append”).Iceberg tables support table properties to configure table behavior, like the default split size for readers. Val results=hiveContext.sql(“select * from orctable”) Val rdd1=hiveContext.sql(s”select * from $dbname.$tab where year between ‘$newyear’ and ‘$year'”) Val lines = omFile(args(1)).getLines.toList Val hiveContext = new .hive.HiveContext(sc) Val sc=new SparkContext(args(0),”SeqtoOrc”) The below code is 10 times faster than Spark SQL. Once the data is converted to ORC format, create an external table having similar structure as that of sequential table but in ORC format and pointing to the output path. These are separted by ~ in the input file. Read the database name,table name, partition dates, output path from the file. Suppose your existing hive table is in sequential format and partitioned by year and month. In this blog, I will detail the code for converting sequence file to orc using spark/scala.
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