diff --git a/README.md b/README.md
index 54f876981b980..33c3344c34508 100644
--- a/README.md
+++ b/README.md
@@ -17,13 +17,10 @@ Buzz Phrases
 Database Support
 ----------------
 
-Panoramix was originally designed on to of Druid.io, but quickly broadened
-to support other databases through the use of SqlAlchemy, a Python
+Panoramix was originally designed on to of Druid.io, but quickly broadened 
+its scope to support other databases through the use of SqlAlchemy, a Python
 ORM that is compatible with 
 [many external databases](http://docs.sqlalchemy.org/en/rel_1_0/core/engines.html). 
-At the moment the SQL has been hard coded to use ``LIMIT``-type
-dialect and needs to be extended to support other syntax 
-(``TOP``, ``ROWNUM``, ...)
 
 What's Druid?
 -------------
diff --git a/app/models.py b/app/models.py
index 7a9b915f80ed1..6dea1f17a9667 100644
--- a/app/models.py
+++ b/app/models.py
@@ -8,16 +8,20 @@
 from dateutil.parser import parse
 from pydruid import client
 from pydruid.utils.filters import Dimension, Filter
+from pandas import read_sql_query
+from sqlalchemy.sql import table, literal_column
+from sqlalchemy import select, and_, text, String
 
 from copy import deepcopy, copy
 from collections import namedtuple
 from datetime import datetime
 import logging
 import json
+import sqlparse
 import requests
 import textwrap
 
-from app import db, get_session
+from app import db, get_session, utils
 
 QueryResult = namedtuple('namedtuple', ['df', 'query', 'duration'])
 
@@ -87,7 +91,7 @@ def metrics_combo(self):
                 for m in self.metrics],
             key=lambda x: x[1])
 
-    def query(
+    def query_bkp(
             self, groupby, metrics,
             granularity,
             from_dttm, to_dttm,
@@ -95,6 +99,11 @@ def query(
             filter=None,
             is_timeseries=True,
             timeseries_limit=15, row_limit=None):
+        """
+        Unused, legacy way of querying by building a SQL string without
+        using the sqlalchemy expression API (new approach which supports
+        all dialects)
+        """
         from pandas import read_sql_query
         qry_start_dttm = datetime.now()
         metrics_exprs = [
@@ -172,6 +181,82 @@ def query(
         return QueryResult(
             df=df, duration=datetime.now() - qry_start_dttm, query=sql)
 
+    def query(
+            self, groupby, metrics,
+            granularity,
+            from_dttm, to_dttm,
+            limit_spec=None,
+            filter=None,
+            is_timeseries=True,
+            timeseries_limit=15, row_limit=None):
+
+        qry_start_dttm = datetime.now()
+        timestamp = literal_column(
+            self.main_datetime_column.column_name).label('timestamp')
+        metrics_exprs = [
+            literal_column(m.expression).label(m.metric_name)
+            for m in self.metrics if m.metric_name in metrics]
+
+        if metrics:
+            main_metric_expr = literal_column(
+                [m.expression for m in self.metrics if m.metric_name == metrics[0]][0])
+        else:
+            main_metric_expr = literal_column("COUNT(*)")
+
+        select_exprs = []
+        groupby_exprs = []
+
+        if groupby:
+            select_exprs = [literal_column(s) for s in groupby]
+            groupby_exprs = [literal_column(s) for s in groupby]
+            inner_groupby_exprs = [literal_column(s).label('__' + s) for s in groupby]
+        select_exprs += metrics_exprs
+        if granularity != "all":
+            select_exprs += [timestamp]
+            groupby_exprs += [timestamp]
+
+        qry = select(select_exprs)
+        from_clause = table(self.table_name)
+        qry = qry.group_by(*groupby_exprs)
+
+        where_clause_and = [
+            timestamp >= from_dttm.isoformat(),
+            timestamp < to_dttm.isoformat(),
+        ]
+        for col, op, eq in filter:
+            if op in ('in', 'not in'):
+                values = eq.split(",")
+                cond = literal_column(col).in_(values)
+                if op == 'not in':
+                    cond = ~cond
+                where_clause_and.append(cond)
+        qry = qry.where(and_(*where_clause_and))
+        qry = qry.limit(row_limit)
+
+        if timeseries_limit and groupby:
+            subq = select(inner_groupby_exprs)
+            subq = subq.select_from(table(self.table_name))
+            subq = subq.where(and_(*where_clause_and))
+            subq = subq.group_by(*inner_groupby_exprs)
+            subq = subq.limit(timeseries_limit)
+            on_clause = []
+            for gb in groupby:
+                on_clause.append(literal_column(s)==literal_column("__" + s))
+
+            from_clause = from_clause.join(subq.alias(), and_(*on_clause))
+
+        qry = qry.select_from(from_clause)
+
+        engine = self.database.get_sqla_engine()
+        sql = str(qry.compile(engine, compile_kwargs={"literal_binds": True}))
+        df = read_sql_query(
+            sql=sql,
+            con=engine
+        )
+        sql = sqlparse.format(sql, reindent=True)
+        return QueryResult(
+            df=df, duration=datetime.now() - qry_start_dttm, query=sql)
+
 
     def fetch_metadata(self):
         table = self.database.get_table(self.table_name)
@@ -421,7 +506,9 @@ def query(
                 }],
             }
             client.groupby(**pre_qry)
+            query_str += "// Two phase query\n// Phase 1\n"
             query_str += json.dumps(client.query_dict, indent=2) + "\n"
+            query_str += "//\nPhase 2 (built based on phase one's results)\n"
             df = client.export_pandas()
             if not df is None and not df.empty:
                 dims = qry['dimensions']
diff --git a/requirements.txt b/requirements.txt
index d6cd8d4b866a1..6819d9960f921 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,3 +8,4 @@ pydruid
 pyhive
 python-dateutil
 requests
+sqlparse