![]() Web log file format standardized by the W3C. A backslash character ( \) is used for escaping.Ī text file with lines delimited by \n. (SOH is ASCII codepoint 1 this format is used by Hive on HDInsight.)Ī text file with tab-separated values ( \t).Ī text file with tab-separated values ( \t). Snappy has been popular in the data world with containers and tools like ORC, Parquet, ClickHouse, BigQuery, Redshift, MariaDB, Cassandra, MongoDB, Lucene and bcolz all offering support. Since the majority of codec do not support split after compression, it is suggested to avoid compressing big files in HDFS. When it comes to the balance of CPU cost and compression ratio, LZ4 or Snappy do a better job. ![]() This format is preferred over JSON, unless the data is non-property bags.Ī text file with pipe-separated values ( |).Ī text file whose entire contents is a single string value.Ī text file with semicolon-separated values ( ).Ī text file with SOH-separated values. Snappy originally made the trade-off going for faster compression and decompression times at the expense of higher compression ratios. lzo.LzoIndexer) to create an index that determines the file splits. Each property bag can be spread on multiple lines. See JSON Lines (JSONL).Ī text file with a JSON array of property bags (each representing a record), or any number of property bags delimited by whitespace, \n or \r\n. See RFC 4180: Common Format and MIME Type for Comma-Separated Values (CSV) Files.Ī text file with JSON objects delimited by \n or \r\n. The following compression codecs are supported: null, deflate (for snappy - use ApacheAvro data format).Ī text file with comma-separated values ( ,). ![]() For information about ingesting Event Hub Capture Avro files, see Ingesting Event Hub Capture Avro files.Ī legacy implementation for AVRO format based on. Reader implementation of the apacheavro format is based on the official Apache Avro library. ![]() The following compression codecs are supported: null, deflate, and snappy. FormatĪn AVRO format with support for logical types. For example, you may find the following validators useful to check CSV or JSON files:įor more information about why ingestion might fail, see Ingestion failures and Ingestion error codes in Azure Data Explorer. We recommend using your preferred validator to confirm the format is valid. Before you ingest data, make sure that your data is properly formatted and defines the expected fields. ![]()
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