feat: Hive partitioned tables support (#375)

* feat: add support for partitioned tables

* feat: import schema class

* fix: update docs

docs/connections.md

@@ -265,6 +265,13 @@ Then `pip install pyodbc`.
 ```
 
 ## Hive
+Please note that for Group By validations, the following property must be set in Hive:
+
+`set hive:hive.groupby.orderby.position.alias=true`
+
+ If you are running Hive on Dataproc, you will also need to run
+ `pip install ibis-framework[impala]`
+
 ```
 {
     # Hive is based off Impala connector

docs/installation.md

@@ -73,7 +73,7 @@ After installing the CLI tool using the instructions below, you will be ready to
 
 ## Deploy Data Validation CLI on your machine
 
-The Data Validation tooling requires Python 3.6+.
+The Data Validation tooling requires Python 3.7+.
 
 ```
 sudo apt-get install python3

third_party/ibis/ibis_impala/api.py

@@ -14,7 +14,9 @@
 
 from ibis.backends.impala import connect
 from ibis.backends.impala import udf
+from ibis.backends.impala.client import ImpalaClient
 import ibis.expr.datatypes as dt
+import ibis.expr.schema as sch
 
 _impala_to_ibis_type = udf._impala_to_ibis_type
 
@@ -61,4 +63,37 @@ def parse_type(t):
             raise Exception(t)
 
 
+def get_schema(self, table_name, database=None):
+    """
+        Return a Schema object for the indicated table and database
+
+        Parameters
+        ----------
+        table_name : string
+          May be fully qualified
+        database : string, default None
+
+        Returns
+        -------
+        schema : ibis Schema
+        """
+    qualified_name = self._fully_qualified_name(table_name, database)
+    query = "DESCRIBE {}".format(qualified_name)
+
+    # only pull out the first two columns which are names and types
+    # pairs = [row[:2] for row in self.con.fetchall(query)]
+    pairs = []
+    for row in self.con.fetchall(query):
+        if row[0] == "":
+            break
+        pairs.append(row[:2])
+
+    names, types = zip(*pairs)
+    ibis_types = [parse_type(type.lower()) for type in types]
+    names = [name.lower() for name in names]
+
+    return sch.Schema(names, ibis_types)
+
+
 udf.parse_type = parse_type
+ImpalaClient.get_schema = get_schema