PERF: Change asset finder to be backed by sqlite3.

Attack the startup bottleneck of creating the asset finders caches for a
large universe, which was between 1-2 seconds on development and
production machines.

Instead, allow the AssetFinder to be passed a sqlite3 file that has
already been populated and then hydrate asset objects only when an
equity is referenced for the first time.

To create aforementioned sqlite3, create an AssetFinder with an db_path
and `create_table` set to True. If `create_table` is set to False, the
prepopulated data in the sqlite file found at db_path will be used.

Default behavior is to use an in memory database.

Behavior that changes:

- Fuzzy lookup now only works on one character, that character needs to be
specified at write/metadata consumption time, since the fuzzy lookup key
is created by dropping the character from each symbol.

- Overwriting partially written metadata is no longer
  supported. i.e. some unit tests allowed for inserting just the identifier,
  and then later updating the symbol, end_date, etc.

  Instead of building an upsert behavior at this time, this patch
  changes the unit tests so that the data for each asset is only
  inserted once.

Other notes:

- populate_cache is now removed, since there is no longer a two step
  process of inserting metadata and then realizing that metadata into
  assets. _spawn_asset is rolled into insert_metadata, so that a call to
  insert_metadata both converts the metadata and makes it available in
  the data store.

tests/test_algorithm.py

@@ -23,6 +23,7 @@ from unittest import TestCase
 import numpy as np
 import pandas as pd
 
+from zipline.assets import AssetFinder
 from zipline.utils.test_utils import (
     nullctx,
     setup_logger,
@@ -1277,16 +1278,22 @@ class TestTradingControls(TestCase):
         df_source, _ = factory.create_test_df_source(self.sim_params)
         metadata = {0: {'start_date': '1990-01-01',
                         'end_date': '2020-01-01'}}
-        algo = SetAssetDateBoundsAlgorithm(asset_metadata=metadata,
-                                           sim_params=self.sim_params,)
+        asset_finder = AssetFinder()
+        algo = SetAssetDateBoundsAlgorithm(
+            asset_finder=asset_finder,
+            asset_metadata=metadata,
+            sim_params=self.sim_params,)
         algo.run(df_source)
 
         # Run the algorithm with a sid that has already ended
         df_source, _ = factory.create_test_df_source(self.sim_params)
         metadata = {0: {'start_date': '1989-01-01',
                         'end_date': '1990-01-01'}}
-        algo = SetAssetDateBoundsAlgorithm(asset_metadata=metadata,
-                                           sim_params=self.sim_params,)
+        asset_finder = AssetFinder()
+        algo = SetAssetDateBoundsAlgorithm(
+            asset_finder=asset_finder,
+            asset_metadata=metadata,
+            sim_params=self.sim_params,)
         with self.assertRaises(TradingControlViolation):
             algo.run(df_source)
 
@@ -1294,8 +1301,10 @@ class TestTradingControls(TestCase):
         df_source, _ = factory.create_test_df_source(self.sim_params)
         metadata = {0: {'start_date': '2020-01-01',
                         'end_date': '2021-01-01'}}
-        algo = SetAssetDateBoundsAlgorithm(asset_metadata=metadata,
-                                           sim_params=self.sim_params,)
+        algo = SetAssetDateBoundsAlgorithm(
+            asset_finder=asset_finder,
+            asset_metadata=metadata,
+            sim_params=self.sim_params,)
         with self.assertRaises(TradingControlViolation):
             algo.run(df_source)