However, beginning with Spark 2.1, Alter Table Partitions is also supported for tables defined using the datasource API. hive> insert into table salesdata partition (date_of_sale) select salesperson_id,product_id,date_of_sale from salesdata_source ; — Please note that the partitioned column should be the last column in the select clause. Table location can also get by running SHOW CREATE TABLE command from hive terminal. https://analyticshut.com/msck-repair-fixing-partitions-in-hive-table Could you please share inputs on better ways. In Hive, tables are created as a directory on HDFS. So, in a file system of hive data (like HDFS), a partition column in a table is literally represented by just having the directory named with the partition value; there are no columns with the value in the data. Hive - Partitioning - Hive organizes tables into partitions. Add partitions to the table, optionally with a custom location for each partition added. This is supported only for tables created using the Hive format. Hive Partition is a way to organize large tables into smaller logical tables based on values of columns; one logical table (partition) for each distinct value. Athena leverages Apache Hive for partitioning data. It turns out that partition columns are implicit in hive. A table can have one or more partitions that correspond to a sub-directory for each partition inside a table directory. A common practice is to partition the data based on time, often leading to a multi-level partitioning scheme. @Manoj Dhake. After the dynamic properties are set as above, to insert value to the “expenses” table, below is the command. Partition is helpful when the table has one or more Partition keys. show partitions in Hive table Partitioned directory in the HDFS for the Hive table But I am sure there is a better way to do it using dataframe functions (not by writing SQL). Insert records into partitioned table in Hive Show partitions in Hive. I am trying get the latest partition (date partition) of a hive table using PySpark-dataframes and done like below. By partitioning your data, you can restrict the amount of data scanned by each query, thus improving performance and reducing cost. If the specified partitions already exist, nothing happens. Users can quickly get the answers for some of their queries by only querying stored … Lets check the partitions for the created table customer_transactions using the show partitions command in Hive. Uses of Hive Table or Partition Statistics. I am new to pySpark. You can partition your data by any key. There are many ways statistics can be useful. It is a way of dividing a table into related parts based on the values of partitioned columns such as date, city, and dep Hive cost based optimizer uses the statistics to generate an optimal query plan. Hive Partitions is a way to organizes tables into partitions by dividing tables into different parts based on partition keys. In general, a SELECT query scans the entire table (other than for sampling).If a table created using the PARTITIONED BY clause, a query can do partition pruning and scan only a fraction of the table relevant to the partitions specified by the query. This solution is scanning through entire data on Hive table to get it. To turn this off set hive.exec.dynamic.partition.mode=nonstrict. SHOW CREATE TABLE table_name; (or) DESCRIBE FORMATTED table_name; Hive Table Partition Location. hive> set hive.exec.max.dynamic.partitions.pernode=1000; //sets the maximum number of dynamic partitions which a mapper or reducer can create, default value is 100. Partition Based Queries. IF NOT EXISTS. Partition keys are basic elements for determining how the data is stored in the table.