dropped pycache files, renaming others

minerva/__init__.py

@@ -1,7 +1,9 @@
-from .minerva import Athena, Execute, Query
+from .minerva import Athena, Execute, Query, Redshift, parallel_map
 
 __all__ = [
     "Execute",
     "Query",
-    "Athena"
+    "Athena",
+    "Redshift",
+    "parallel_map"
 ]

minerva/athena.py

@@ -6,6 +6,7 @@ import pyarrow as pa
 import pyarrow.dataset
 import pprint
 import datetime
+from minerva import parallel_map
 
 pp = pprint.PrettyPrinter(indent=4)
 
@@ -19,7 +20,7 @@ class Athena:
         q = Query(self, sql)
         q.run()
         return q
-    
+
     def execute(self, sql):
         e = Execute(self, sql)
         e.run()
@@ -42,13 +43,13 @@ class Execute:
         self.handler = handler
         self.athena  = handler.athena
         self.sql     = sql
+        self.info_cache = None
+
+    def query(self):
+        return self.sql
 
     def run(self):
-        out    = os.path.join(self.handler.output,
-                              str(random.random()))
-        config = {"OutputLocation": out}
-        resp  = self.athena.start_query_execution(QueryString=self.sql,
-                                                  ResultConfiguration=config)
+        resp  = self.athena.start_query_execution(QueryString=self.sql)
         self.query_id = resp['QueryExecutionId']
         return resp
 
@@ -57,75 +58,52 @@ class Execute:
 
     def info(self):
         res = self.athena.get_query_execution(QueryExecutionId=self.query_id)
-        return res['QueryExecution']
+        self.info_cache = res['QueryExecution']
+        return self.info_cache
 
-    def execute(self):
-        tiedot = self.info()
-        status = tiedot['Status']['State']
-
-        while status in ['QUEUED', 'RUNNING']:
+    def finish(self):
+        while stat := self.status() in ['QUEUED', 'RUNNING']:
             time.sleep(5)
-            tiedot = self.info()
-            status = tiedot['Status']['State']
 
-        return status # finalized state
+        return stat # finalized state
 
 
-class Query:
+class Query(Execute):
     DATA_STYLE = 'parquet'
 
-    def __init__(self, handler, sql):
-        self.handler = handler
-        self.athena  = handler.athena
-        self.sql     = sql
-
-    def run(self):
+    def query(self):
         out    = os.path.join(self.handler.output,
                               str(random.random()))
-        config = {"OutputLocation": out}
         query  = f"unload ({self.sql}) to {repr(out)} " + \
                  f"with (format = '{self.DATA_STYLE}')"
+        return query
 
-        resp  = self.athena.start_query_execution(QueryString=query,
-                                                  ResultConfiguration=config)
-        self.query_id = resp['QueryExecutionId']
-        return resp
-
-    def status(self):
-        return self.info()['Status']
-
-    def info(self):
-        res = self.athena.get_query_execution(QueryExecutionId=self.query_id)
-        return res['QueryExecution']
-
-    def results(self):
-        tiedot = self.info()
-        status = tiedot['Status']['State']
-
-        while status in ['QUEUED', 'RUNNING']:
-            time.sleep(5)
-            tiedot = self.info()
-            status = tiedot['Status']['State']
+    def manifest_files(self):
+        status = self.finish()
 
         if status == "SUCCEEDED":
-            # Because we're using `UNLOAD`, we get a manifest of the files
-            # that make up our data.
-            files   = self.manifest(tiedot).strip().split("\n")
-            files   = [f.strip() for f in files if f.strip()] # filter empty
-
-            # TODO parallelize this
-            local   = [self.handler.download(f) for f in files]
-            self.ds = pa.dataset.dataset(local)
-
-            ms = tiedot['Statistics']['TotalExecutionTimeInMillis']
+            # Track the runtime
+            ms = self.info_cache['Statistics']['TotalExecutionTimeInMillis']
             self.runtime = datetime.timedelta(seconds=ms / 1000)
 
-            return self.ds
+            # Because we're using `UNLOAD`, we get a manifest of the files
+            # that make up our data.
+            manif = self.info_cache['Statistics']['DataManifestLocation']
+            tmp   = self.handler.download(manif)
+            with open(tmp, "r") as f:
+                txt = f.read()
+
+            files = txt.strip().split("\n")
+            files = [f.strip() for f in files if f.strip()] # filter empty
+
+            return files
         else:
             return status # canceled or error
 
-    def manifest(self, tiedot):
-        manif = tiedot['Statistics']['DataManifestLocation']
-        tmp   = self.handler.download(manif)
-        with open(tmp, "r") as f:
-            return f.read()
+    # TODO parallelize this
+    def results(self):
+        #local   = [self.handler.download(f) for f in self.manifest_files()]
+        local   = parallel_map(self.handler.download, self.manifest_files())
+        self.ds = pa.dataset.dataset(local)
+        return self.ds
+

minerva/parallel.py

@@ -0,0 +1,21 @@
+from joblib import Parallel, delayed
+
+# Instead of taking each object in the list and giving it its own thread,
+# this splits the list into `cores` groups and gives each group its own
+# thread, where the group is now processed in series within its thread.
+#
+# Example:
+#   def say(stuff):
+#       print(stuff)
+#
+#   parallel_map(say, [str(i) for i in range(10)], cores=4)
+def parallel_map(func=None, data=None, cores=8):
+    size = len(data) // cores
+    groups = [data[i:i + size] for i in range(0, len(data), size)]
+
+    def wrapper_func(fs):
+        return [func(f) for f in fs]
+
+    res = Parallel(n_jobs=cores)(delayed(wrapper_func)(group) for group in groups)
+
+    return [val for r in res for val in r]

minerva/redshift.py

@@ -0,0 +1,114 @@
+import boto3
+import os
+import random
+import time
+import pyarrow as pa
+import pyarrow.dataset
+import pprint
+import datetime
+from minerva import parallel_map
+
+pp = pprint.PrettyPrinter(indent=4)
+
+class Redshift:
+    def __init__(self, profile, output, db=None, cluster=None):
+        self.session  = boto3.session.Session(profile_name=profile)
+        self.redshift = self.session.client("redshift-data")
+        self.output   = output
+        self.database = db
+        self.cluster  = cluster
+
+    def query(self, sql):
+        q = Query(self, sql)
+        q.run()
+        return q
+
+    def execute(self, sql):
+        e = Execute(self, sql)
+        e.run()
+        return e
+
+    def download(self, s3):
+        bucket = s3.split("/")[2]
+        file   = os.path.join(*s3.split("/")[3:])
+        tmp    = f"/tmp/{random.random()}.bin"
+        self.session.client('s3').download_file(bucket, file, tmp)
+
+        return tmp
+
+class Execute:
+    """
+    Execute will not return results, but will execute the SQL and return the final state.
+    Execute is meant to be used for DML statements such as CREATE DATABASE/TABLE
+    """
+    def __init__(self, handler, sql):
+        self.handler    = handler
+        self.redshift   = handler.redshift
+        self.sql        = sql
+        self.info_cache = None
+
+    def query(self):
+        return self.sql
+
+    def run(self):
+        resp  = self.redshift.execute_statement(Sql=self.query(),
+                                                Database=self.handler.database,
+                                                ClusterIdentifier=self.handler.cluster)
+        self.query_id = resp['Id']
+        return resp
+
+    def status(self):
+        return self.info()['Status']
+
+    def info(self):
+        res = self.redshift.describe_statement(Id=self.query_id)
+        self.info_cache = res
+        return self.info_cache
+
+    def finish(self):
+        while stat := self.status() in ['SUBMITTED', 'PICKED', 'STARTED']:
+            time.sleep(5)
+
+        return stat # finalized state
+
+
+class Query(Execute):
+    DATA_STYLE = 'parquet'
+
+    def query(self):
+        self.out = os.path.join(self.handler.output,
+                              str(random.random()))
+        query    = f"unload ({repr(self.sql)}) to {repr(self.out)} " + \
+                   f"iam_role default " + \
+                   f"format as {self.DATA_STYLE} " + \
+                   f"manifest"
+        return query
+
+    def manifest_files(self):
+        status = self.finish()
+
+        if status == "SUCCEEDED":
+            # Track the runtime
+            self.runtime = tiedot['UpdatedAt'] - tiedot['CreatedAt']
+
+            # Because we're using `UNLOAD`, we get a manifest of the files
+            # that make up our data.
+            manif = self.out + "manifest"
+            tmp   = self.handler.download(manif)
+            with open(tmp, "r") as f:
+                js = json.load(f)
+
+            # Filter empty strings
+            files = [e['url'].strip() for e in js['entries'] if e['url'].strip()]
+
+            return files
+        else:
+            return status # canceled or error
+
+    # TODO parallelize this
+    def results(self):
+        #local   = [self.handler.download(f) for f in self.manifest_files()]
+        local   = parallel_map(self.handler.download, self.manifest_files())
+        self.ds = pa.dataset.dataset(local)
+        return self.ds
+

pyproject.toml

@@ -3,7 +3,8 @@ name = "minerva"
 version = "0.2.1"
 description = "Easier access to AWS Athena and Redshift"
 authors = [
-  "Ari Brown <ari@airintech.com>"
+  "Ari Brown <ari@airintech.com>",
+  "Roshan Punnoose <roshan.punnoose@jhuapl.edu>"
 ]
 packages = [
   { include = "minerva/**/*.py"}

test.py

@@ -1,9 +1,9 @@
-import minerva.minerva as a
+import minerva
 import pprint
 
 pp = pprint.PrettyPrinter(indent=4)
 
-athena = a.Athena("hay", "s3://haystac-pmo-athena/")
+athena = Athena("hay", "s3://haystac-pmo-athena/")
 #query  = athena.query(
 #"""SELECT *
 #FROM trajectories.kitware

test2.py

@@ -1,11 +1,11 @@
-import minerva.blueshift as b
+import minerva
 import pprint
 
 pp = pprint.PrettyPrinter(indent=4)
 
-red   = b.Redshift("hay", "s3://haystac-pmo-athena/",
-                   db="dev",
-                   cluster="redshift-cluster-1")
+red   = Redshift("hay", "s3://haystac-pmo-athena/",
+                 db="dev",
+                 cluster="redshift-cluster-1")
 query = red.query("select count(*) from myspectrum_schema.kitware")
 print(query)
 data  = query.results()