Snappy compression format full#

Snappy is the current default compression used by the Wired Tiger storage engine for block and journal compression and also by the MongoDB server for client communications.Ĭompression is a tradeoff between how much CPU is used to perform the compression post data insertion and how much that CPU works results in more compressed data. MongoDB already has support for snappy and Zlib compression of data. To put this in context, this is the third compression option supported by MongoDB. Zstd compression is a new feature in MongoDB 4.2. Zlib - > High compression (like gzip) / high resorce consumption Snappy(Default) - > Less compression ratio compared to Zlip / less resource consumptionĢ.

set ( '.class', '. 4.2, MongoDB supports two types of compression.ġ. Updated by Hive or other external tools, you need to refresh them manually to ensure consistent When Hive metastore Parquet tableĬonversion is enabled, metadata of those converted tables are also cached.

Spark SQL caches Parquet metadata for better performance. Any fields that only appear in the Hive metastore schema are added as nullable field in the.Any fields that only appear in the Parquet schema are dropped in the reconciled schema.The reconciled schema contains exactly those fields defined in Hive metastore schema. The reconciled field should have the data type of the Parquet side, so that The reconciliation rules are:įields that have the same name in both schema must have the same data type regardless of Hive metastore Parquet table to a Spark SQL Parquet table. Hive considers all columns nullable, while nullability in Parquet is significantĭue to this reason, we must reconcile Hive metastore schema with Parquet schema when converting a.Hive is case insensitive, while Parquet is not.There are two key differences between Hive and Parquet from the perspective of table schema This behavior is controlled by the Ĭonfiguration, and is turned on by default. Parquet tables, Spark SQL will try to use its own Parquet support instead of Hive SerDe forīetter performance. When reading from Hive metastore Parquet tables and writing to non-partitioned Hive metastore

Snappy compression format full#

# with the partitioning column appeared in the partition directory paths.įind full example code at 'examples/src/main/r/RSparkSQLExample.R' in the Spark repo. printSchema () # The final schema consists of all 3 columns in the Parquet files together parquet ( 'data/test_table/key=2' ) # Read the partitioned table map ( lambda i : Row ( single = i, triple = i ** 3 ))) cubesDF. # adding a new column and dropping an existing columnĬubesDF = spark. parquet ( 'data/test_table/key=1' ) # Create another DataFrame in a new partition directory, map ( lambda i : Row ( single = i, double = i ** 2 ))) squaresDF. # Create a simple DataFrame, stored into a partition directory printSchema () // The final schema consists of all 3 columns in the Parquet files together // with the partitioning column appeared in the partition directory paths // root // |- value: int (nullable = true) // |- square: int (nullable = true) // |- cube: int (nullable = true) // |- key: int (nullable = true)įrom pyspark.sql import Row # spark is from the previous example. parquet ( 'data/test_table/key=2' ) // Read the partitioned table Dataset mergedDF = spark. parquet ( 'data/test_table/key=1' ) List cubes = new ArrayList () for ( int value = 6 value cubesDF = spark. Here we prefix all the names with 'Name:' schema <- structType ( structField ( 'name', 'string' )) teenNames <- dapply ( df, function ( p ) List squares = new ArrayList () for ( int value = 1 value squaresDF = spark. show () // +-+ // | value| // +-+ // |Name: Justin| // +-+ĭf = 13 AND age <= 19' ) head ( teenagers ) # name # 1 Justin # We can also run custom R-UDFs on Spark DataFrames. map ( ( MapFunction ) row -> 'Name: ' + row. sql ( 'SELECT name FROM parquetFile WHERE age BETWEEN 13 AND 19' ) Dataset namesDS = namesDF. createOrReplaceTempView ( 'parquetFile' ) Dataset namesDF = spark. parquet ( 'people.parquet' ) // Parquet files can also be used to create a temporary view and then used in SQL statements parquetFileDF. Parquet files are self-describing so the schema is preserved // The result of loading a parquet file is also a DataFrame Dataset parquetFileDF = spark. parquet ( 'people.parquet' ) // Read in the Parquet file created above. json ( 'examples/src/main/resources/people.json' ) // DataFrames can be saved as Parquet files, maintaining the schema information peopleDF. Import. import .Encoders import .Dataset import .Row Dataset peopleDF = spark.