diff --git a/datasette/app.py b/datasette/app.py
index 460464ab..a4b8f7f0 100644
--- a/datasette/app.py
+++ b/datasette/app.py
@@ -579,6 +579,7 @@ class Datasette:
truncate=False,
custom_time_limit=None,
page_size=None,
+ log_sql_errors=True,
):
"""Executes sql against db_name in a thread"""
page_size = page_size or self.page_size
@@ -605,11 +606,12 @@ class Datasette:
except sqlite3.OperationalError as e:
if e.args == ('interrupted',):
raise InterruptedError(e)
- print(
- "ERROR: conn={}, sql = {}, params = {}: {}".format(
- conn, repr(sql), params, e
+ if log_sql_errors:
+ print(
+ "ERROR: conn={}, sql = {}, params = {}: {}".format(
+ conn, repr(sql), params, e
+ )
)
- )
raise
if truncate:
diff --git a/datasette/facets.py b/datasette/facets.py
index a1467d4d..f1e4bbe7 100644
--- a/datasette/facets.py
+++ b/datasette/facets.py
@@ -1,17 +1,48 @@
from sanic.request import RequestParameters
import urllib
+import re
from datasette import hookimpl
from datasette.utils import (
escape_sqlite,
+ get_all_foreign_keys,
path_with_added_args,
path_with_removed_args,
- detect_json1
+ detect_json1,
+ InvalidSql,
+ sqlite3,
)
+def load_facet_configs(request, table_metadata):
+ # Given a request and this tables metadata, return
+ # a dict of selected facets and their configs
+ # return {type, [config1, config2]...}
+ facet_configs = {}
+ #metadata_facets = table_metadata.get("facets", [])
+ #facets = metadata_facets[:]
+ args = RequestParameters(
+ urllib.parse.parse_qs(request.query_string, keep_blank_values=True)
+ )
+ for key, values in args.items():
+ if key.startswith("_facet"):
+ # Figure out the facet type
+ if key == "_facet":
+ type = "column"
+ elif key.startswith("_facet_"):
+ type = key[len("_facet_"):]
+ for value in values:
+ # The value is the config - either JSON or not
+ if value.startswith("{"):
+ config = json.loads(value)
+ else:
+ config = {"single": value}
+ facet_configs.setdefault(type, []).append(config)
+ return facet_configs
+
+
@hookimpl
def register_facet_classes():
- return [ColumnFacet]
+ return [ColumnFacet, ArrayFacet, ManyToManyFacet, DateFacet, EmojiFacet, PhrasesFacet]
# classes = [ColumnFacet, ManyToManyFacet]
# if detect_json1():
# classes.append(ArrayFacet)
@@ -28,26 +59,30 @@ class Facet:
self.table = table # can be None
self.configs = configs
- async def suggest(self, sql, params):
- raise NotImplementedError
+ async def suggest(self, sql, params, filtered_table_rows_count):
+ return []
async def facet_results(self, sql, params):
# returns ([results], [timed_out])
+ # TODO: Include "hideable" with each one somehow, which indicates if it was
+ # defined in metadata (in which case you cannot turn it off)
raise NotImplementedError
+ async def get_columns(self, sql, params=None):
+ return (
+ await self.ds.execute(
+ self.database, "select * from ({}) limit 0".format(sql),
+ params or []
+ )
+ ).columns
+
class ColumnFacet(Facet):
- # This is the default so type=""
- type = ""
+ type = "column"
async def suggest(self, sql, params, filtered_table_rows_count):
- # Detect column names
- columns = (
- await self.ds.execute(
- self.database, "select * from ({}) limit 0".format(sql),
- params
- )
- ).columns
+ # Detect column names using the "limit 0" trick
+ columns = await self.get_columns(sql, params)
facet_size = self.ds.config("default_facet_size")
suggested_facets = []
for column in columns:
@@ -104,7 +139,9 @@ class ColumnFacet(Facet):
other_args[key] = value[0]
facet_size = self.ds.config("default_facet_size")
- for column in self.configs:
+ for config in (self.configs or []):
+ column = config.get("column") or config["single"]
+ # TODO: does this query break if inner sql produces value or count columns?
facet_sql = """
select {col} as value, count(*) as count from (
{sql}
@@ -166,6 +203,460 @@ class ColumnFacet(Facet):
class ManyToManyFacet(Facet):
type = "m2m"
+ async def suggest(self, sql, params, filtered_table_rows_count):
+ # This is calculated based on foreign key relationships to this table
+ # Are there any many-to-many tables pointing here?
+ suggested_facets = []
+ all_foreign_keys = await self.ds.execute_against_connection_in_thread(
+ self.database, get_all_foreign_keys
+ )
+ incoming = all_foreign_keys[self.table]["incoming"]
+ # Do any of these incoming tables have exactly two outgoing keys?
+ for fk in incoming:
+ other_table = fk["other_table"]
+ other_table_outgoing_foreign_keys = all_foreign_keys[other_table]["outgoing"]
+ if len(other_table_outgoing_foreign_keys) == 2:
+ suggested_facets.append({
+ "name": other_table,
+ "type": "m2m",
+ "toggle_url": self.ds.absolute_url(
+ self.request, path_with_added_args(
+ self.request, {"_facet_m2m": other_table}
+ )
+ ),
+ })
+ return suggested_facets
+
+ async def facet_results(self, *args, **kwargs):
+
+ return [], []
+
class ArrayFacet(Facet):
type = "array"
+
+ async def suggest(self, sql, params, filtered_table_rows_count):
+ columns = await self.get_columns(sql, params)
+ suggested_facets = []
+ for column in columns:
+ # Is every value in this column either null or a JSON array?
+ suggested_facet_sql = """
+ select distinct json_type({column})
+ from ({sql})
+ """.format(
+ column=escape_sqlite(column),
+ sql=sql,
+ )
+ try:
+ results = await self.ds.execute(
+ self.database, suggested_facet_sql, params,
+ truncate=False,
+ custom_time_limit=self.ds.config("facet_suggest_time_limit_ms"),
+ log_sql_errors=False,
+ )
+ types = tuple(r[0] for r in results.rows)
+ if types in (
+ ("array",),
+ ("array", None)
+ ):
+ suggested_facets.append({
+ "name": column,
+ "type": "array",
+ "toggle_url": self.ds.absolute_url(
+ self.request, path_with_added_args(
+ self.request, {"_facet_array": column}
+ )
+ ),
+ })
+ except (InterruptedError, sqlite3.OperationalError):
+ continue
+ return suggested_facets
+
+ async def facet_results(self, sql, params):
+ # self.configs should be a plain list of columns
+ facet_results = {}
+ facets_timed_out = []
+
+ # TODO: refactor this
+ args = RequestParameters(
+ urllib.parse.parse_qs(self.request.query_string, keep_blank_values=True)
+ )
+ other_args = {}
+ for key, value in args.items():
+ if key.startswith("_") and "__" not in key:
+ pass
+ else:
+ other_args[key] = value[0]
+
+ facet_size = self.ds.config("default_facet_size")
+ for config in (self.configs or []):
+ column = config.get("column") or config["single"]
+ facet_sql = """
+ select j.value as value, count(*) as count from (
+ {sql}
+ ) join json_each({col}) j
+ group by j.value order by count desc limit {limit}
+ """.format(
+ col=escape_sqlite(column),
+ sql=sql,
+ limit=facet_size+1,
+ )
+ try:
+ facet_rows_results = await self.ds.execute(
+ self.database, facet_sql, params,
+ truncate=False,
+ custom_time_limit=self.ds.config("facet_time_limit_ms"),
+ )
+ facet_results_values = []
+ facet_results[column] = {
+ "name": column,
+ "results": facet_results_values,
+ "truncated": len(facet_rows_results) > facet_size,
+ }
+ facet_rows = facet_rows_results.rows[:facet_size]
+ for row in facet_rows:
+ selected = str(other_args.get(column)) == str(row["value"])
+ if selected:
+ toggle_path = path_with_removed_args(
+ self.request, {"{}__arraycontains".format(column): str(row["value"])}
+ )
+ else:
+ toggle_path = path_with_added_args(
+ self.request, {"{}__arraycontains".format(column): row["value"]}
+ )
+ facet_results_values.append({
+ "value": row["value"],
+ "label": row["value"],
+ "count": row["count"],
+ "toggle_url": self.ds.absolute_url(self.request, toggle_path),
+ "selected": selected,
+ })
+ except InterruptedError:
+ facets_timed_out.append(column)
+
+ return facet_results, facets_timed_out
+
+
+
+class DateFacet(Facet):
+ type = "date"
+
+ async def suggest(self, sql, params, filtered_table_rows_count):
+ columns = await self.get_columns(sql, params)
+ suggested_facets = []
+ for column in columns:
+ # Does this column contain any dates in the first 100 rows?
+ suggested_facet_sql = """
+ select date({column}) from (
+ {sql}
+ ) limit 100;
+ """.format(
+ column=escape_sqlite(column),
+ sql=sql,
+ )
+ try:
+ results = await self.ds.execute(
+ self.database, suggested_facet_sql, params,
+ truncate=False,
+ custom_time_limit=self.ds.config("facet_suggest_time_limit_ms"),
+ log_sql_errors=False,
+ )
+ values = tuple(r[0] for r in results.rows)
+ if (any(values)):
+ suggested_facets.append({
+ "name": column,
+ "type": "date",
+ "toggle_url": self.ds.absolute_url(
+ self.request, path_with_added_args(
+ self.request, {"_facet_date": column}
+ )
+ ),
+ })
+ except (InterruptedError, sqlite3.OperationalError):
+ continue
+ return suggested_facets
+
+ async def facet_results(self, sql, params):
+ # self.configs should be a plain list of columns
+ facet_results = {}
+ facets_timed_out = []
+
+ # TODO: refactor this
+ args = RequestParameters(
+ urllib.parse.parse_qs(self.request.query_string, keep_blank_values=True)
+ )
+ other_args = {}
+ for key, value in args.items():
+ if key.startswith("_") and "__" not in key:
+ pass
+ else:
+ other_args[key] = value[0]
+
+ facet_size = self.ds.config("default_facet_size")
+ for config in (self.configs or []):
+ column = config.get("column") or config["single"]
+ # TODO: does this query break if inner sql produces value or count columns?
+ facet_sql = """
+ select date({col}) as value, count(*) as count from (
+ {sql}
+ )
+ where date({col}) is not null
+ group by date({col}) order by date({col}) desc limit {limit}
+ """.format(
+ col=escape_sqlite(column),
+ sql=sql,
+ limit=facet_size+1,
+ )
+ try:
+ facet_rows_results = await self.ds.execute(
+ self.database, facet_sql, params,
+ truncate=False,
+ custom_time_limit=self.ds.config("facet_time_limit_ms"),
+ )
+ facet_results_values = []
+ facet_results[column] = {
+ "name": column,
+ "results": facet_results_values,
+ "truncated": len(facet_rows_results) > facet_size,
+ }
+ facet_rows = facet_rows_results.rows[:facet_size]
+ for row in facet_rows:
+ selected = str(other_args.get("{}__date".format(column))) == str(row["value"])
+ if selected:
+ toggle_path = path_with_removed_args(
+ self.request, {"{}__date".format(column): str(row["value"])}
+ )
+ else:
+ toggle_path = path_with_added_args(
+ self.request, {"{}__date".format(column): row["value"]}
+ )
+ facet_results_values.append({
+ "value": row["value"],
+ "label": row["value"],
+ "count": row["count"],
+ "toggle_url": self.ds.absolute_url(self.request, toggle_path),
+ "selected": selected,
+ })
+ except InterruptedError:
+ facets_timed_out.append(column)
+
+ return facet_results, facets_timed_out
+
+
+
+class PhrasesFacet(Facet):
+ type = "phrases"
+
+ async def facet_results(self, sql, params):
+ # Hmm... for this one we actually need the column name(s) AND the word list
+ # Current design supports one of the following:
+ # ?_facet_phrases=column:word1,word2,word3
+ # which means we could support multiple columns like so:
+ # ?_facet_phrases=column1:column2:word1,word2,word3
+ # As JSON:
+ # ?_facet_phrases={"columns":["column1","column2"],"phrases":["word1","word2"]}
+ # Urgh, the filter option when one is selected is going to be pretty nasty
+ facet_results = {}
+ facets_timed_out = []
+
+ facet_size = self.ds.config("default_facet_size")
+ for config in (self.configs or []):
+ if isinstance(config, dict) and "single" in config:
+ config = config["single"]
+ if isinstance(config, str):
+ columns = config.rsplit(":", 1)[0].split(":")
+ phrases = config.rsplit(":", 1)[1].split(",")
+ else:
+ columns = config["columns"]
+ phases = config["phrases"]
+ # FOR THE MOMENT only support one column
+ column = columns[0]
+ facet_sql = """
+ select count(*) as count, j.value as value
+ from (
+ select extract_phrases_json({col}, '{json_phrases}') as a from (
+ {sql}
+ )
+ )
+ join json_each(a) j
+ group by j.value order by count desc limit {limit}
+ """.format(
+ col=escape_sqlite(column),
+ sql=sql,
+ # TODO: this will break if any phrases contain '
+ json_phrases=json.dumps(phrases),
+ limit=facet_size+1,
+ )
+ try:
+ facet_rows_results = await self.ds.execute(
+ self.database, facet_sql, params,
+ truncate=False,
+ custom_time_limit=self.ds.config("facet_time_limit_ms"),
+ )
+ facet_results_values = []
+ facet_results[column] = {
+ "name": column,
+ "results": facet_results_values,
+ "truncated": len(facet_rows_results) > facet_size,
+ }
+ facet_rows = facet_rows_results.rows[:facet_size]
+ for row in facet_rows:
+ facet_results_values.append({
+ "value": row["value"],
+ "label": row["value"],
+ "count": row["count"],
+ # TODO: toggle_url for selected
+ "toggle_url": "", # self.ds.absolute_url(self.request, toggle_path),
+ # TODO: identify selected
+ "selected": False,
+ })
+ except InterruptedError:
+ facets_timed_out.append(column)
+
+ return facet_results, facets_timed_out
+
+
+class EmojiFacet(Facet):
+ type = "emoji"
+
+ async def suggest(self, sql, params, filtered_table_rows_count):
+ columns = await self.get_columns(sql, params)
+ suggested_facets = []
+ for column in columns:
+ # Is every value in this column either null or a JSON array?
+ suggested_facet_sql = """
+ select extract_emoji({column}) as emojis
+ from ({sql}) where emojis != "" limit 1
+ """.format(
+ column=escape_sqlite(column),
+ sql=sql,
+ )
+ try:
+ results = await self.ds.execute(
+ self.database, suggested_facet_sql, params,
+ truncate=False,
+ custom_time_limit=self.ds.config("facet_suggest_time_limit_ms"),
+ log_sql_errors=True,
+ )
+ if results.rows:
+ suggested_facets.append({
+ "name": column,
+ "type": "emoji",
+ "toggle_url": self.ds.absolute_url(
+ self.request, path_with_added_args(
+ self.request, {"_facet_emoji": column}
+ )
+ ),
+ })
+ except (InterruptedError, sqlite3.OperationalError) as e:
+ print(" oh no ", e)
+ continue
+ return suggested_facets
+
+ async def facet_results(self, *args, **kwargs):
+ return [], []
+
+
+@hookimpl
+def prepare_connection(conn):
+ conn.create_function("extract_emoji", 1, extract_emoji)
+ conn.create_function("extract_emoji_json", 1, extract_emoji_json)
+ conn.create_function("extract_phrases_json", 2, extract_phrases_json)
+ conn.create_function("extract_name_json", 1, extract_name_json)
+ conn.create_function("decode_punycode", 1, decode_punycode)
+
+
+import json
+
+def extract_emoji(s):
+ if not isinstance(s, str):
+ return ""
+ try:
+ return "".join(emoji_re.findall(s))
+ except Exception as e:
+ print(e)
+ raise
+
+
+def extract_emoji_json(s):
+ try:
+ if not isinstance(s, str):
+ return "[]"
+ return json.dumps(list(set([
+ c.encode("punycode").decode("latin1") for c in emoji_re.findall(s)
+ ])))
+ except Exception as e:
+ print(e)
+ raise
+
+
+def extract_name_json(s):
+ try:
+ if not isinstance(s, str):
+ return "[]"
+ return json.dumps(list(set([m.group(0) for m in name_re.finditer(s)])))
+ except Exception as e:
+ print(e)
+ raise
+
+
+def extract_phrases_json(s, phrases):
+ # phrases is a '["json", "list", "of", "phrases"]'
+ if not isinstance(s, str):
+ return "[]"
+ phrases_list = json.loads(phrases)
+ # I tried caching the regex but the performance boost was negligible
+ r = re.compile(r"\b{}\b".format("|".join(phrases_list)), re.I)
+ return json.dumps(list(set(w.lower() for w in r.findall(s))))
+
+
+name_re = re.compile("([A-Z][a-z]+)+( [A-Z][a-z]+)")
+
+
+
+def decode_punycode(s):
+ return s.encode("latin1").decode("punycode")
+
+
+emoji_re = re.compile(
+ "[\xa9\xae\u203c\u2049\u2122\u2139\u2194-\u2199\u21a9-\u21aa\u231a-\u231b"
+ "\u2328\u23cf\u23e9-\u23f3\u23f8-\u23fa\u24c2\u25aa-\u25ab\u25b6\u25c0"
+ "\u25fb-\u25fe\u2600-\u2604\u260e\u2611\u2614-\u2615\u2618\u261d\u2620"
+ "\u2622-\u2623\u2626\u262a\u262e-\u262f\u2638-\u263a\u2640\u2642\u2648-"
+ "\u2653\u2660\u2663\u2665-\u2666\u2668\u267b\u267f\u2692-\u2697\u2699"
+ "\u269b-\u269c\u26a0-\u26a1\u26aa-\u26ab\u26b0-\u26b1\u26bd-\u26be\u26c4-"
+ "\u26c5\u26c8\u26ce\u26cf\u26d1\u26d3-\u26d4\u26e9-\u26ea\u26f0-\u26f5"
+ "\u26f7-\u26fa\u26fd\u2702\u2705\u2708-\u2709\u270a-\u270b\u270c-\u270d"
+ "\u270f\u2712\u2714\u2716\u271d\u2721\u2728\u2733-\u2734\u2744\u2747\u274c"
+ "\u274e\u2753-\u2755\u2757\u2763-\u2764\u2795-\u2797\u27a1\u27b0\u27bf"
+ "\u2934-\u2935\u2b05-\u2b07\u2b1b-\u2b1c\u2b50\u2b55\u3030\u303d\u3297"
+ "\u3299\U0001f004\U0001f0cf\U0001f170-\U0001f171\U0001f17e\U0001f17f"
+ "\U0001f18e\U0001f191-\U0001f19a\U0001f1e6-\U0001f1ff\U0001f201-\U0001f202"
+ "\U0001f21a\U0001f22f\U0001f232-\U0001f23a\U0001f250-\U0001f251\U0001f300-"
+ "\U0001f320\U0001f321\U0001f324-\U0001f32c\U0001f32d-\U0001f32f\U0001f330-"
+ "\U0001f335\U0001f336\U0001f337-\U0001f37c\U0001f37d\U0001f37e-\U0001f37f"
+ "\U0001f380-\U0001f393\U0001f396-\U0001f397\U0001f399-\U0001f39b\U0001f39e-"
+ "\U0001f39f\U0001f3a0-\U0001f3c4\U0001f3c5\U0001f3c6-\U0001f3ca\U0001f3cb-"
+ "\U0001f3ce\U0001f3cf-\U0001f3d3\U0001f3d4-\U0001f3df\U0001f3e0-\U0001f3f0"
+ "\U0001f3f3-\U0001f3f5\U0001f3f7\U0001f3f8-\U0001f3ff\U0001f400-\U0001f43e"
+ "\U0001f43f\U0001f440\U0001f441\U0001f442-\U0001f4f7\U0001f4f8\U0001f4f9-"
+ "\U0001f4fc\U0001f4fd\U0001f4ff\U0001f500-\U0001f53d\U0001f549-\U0001f54a"
+ "\U0001f54b-\U0001f54e\U0001f550-\U0001f567\U0001f56f-\U0001f570\U0001f573-"
+ "\U0001f579\U0001f57a\U0001f587\U0001f58a-\U0001f58d\U0001f590\U0001f595-"
+ "\U0001f596\U0001f5a4\U0001f5a5\U0001f5a8\U0001f5b1-\U0001f5b2\U0001f5bc"
+ "\U0001f5c2-\U0001f5c4\U0001f5d1-\U0001f5d3\U0001f5dc-\U0001f5de\U0001f5e1"
+ "\U0001f5e3\U0001f5e8\U0001f5ef\U0001f5f3\U0001f5fa\U0001f5fb-\U0001f5ff"
+ "\U0001f600\U0001f601-\U0001f610\U0001f611\U0001f612-\U0001f614\U0001f615"
+ "\U0001f616\U0001f617\U0001f618\U0001f619\U0001f61a\U0001f61b\U0001f61c-"
+ "\U0001f61e\U0001f61f\U0001f620-\U0001f625\U0001f626-\U0001f627\U0001f628-"
+ "\U0001f62b\U0001f62c\U0001f62d\U0001f62e-\U0001f62f\U0001f630-\U0001f633"
+ "\U0001f634\U0001f635-\U0001f640\U0001f641-\U0001f642\U0001f643-\U0001f644"
+ "\U0001f645-\U0001f64f\U0001f680-\U0001f6c5\U0001f6cb-\U0001f6cf\U0001f6d0"
+ "\U0001f6d1-\U0001f6d2\U0001f6e0-\U0001f6e5\U0001f6e9\U0001f6eb-\U0001f6ec"
+ "\U0001f6f0\U0001f6f3\U0001f6f4-\U0001f6f6\U0001f6f7-\U0001f6f8\U0001f910-"
+ "\U0001f918\U0001f919-\U0001f91e\U0001f91f\U0001f920-\U0001f927\U0001f928-"
+ "\U0001f92f\U0001f930\U0001f931-\U0001f932\U0001f933-\U0001f93a\U0001f93c-"
+ "\U0001f93e\U0001f940-\U0001f945\U0001f947-\U0001f94b\U0001f94c\U0001f950-"
+ "\U0001f95e\U0001f95f-\U0001f96b\U0001f980-\U0001f984\U0001f985-\U0001f991"
+ "\U0001f992-\U0001f997\U0001f9c0\U0001f9d0-\U0001f9e6]"
+)
diff --git a/datasette/templates/table.html b/datasette/templates/table.html
index 1c65aa10..730a78ff 100644
--- a/datasette/templates/table.html
+++ b/datasette/templates/table.html
@@ -110,7 +110,7 @@
{% if suggested_facets %}
- Suggested facets: {% for facet in suggested_facets %}{{ facet.name }}{% if not loop.last %}, {% endif %}{% endfor %}
+ Suggested facets: {% for facet in suggested_facets %}{{ facet.name }}{% if facet.type %} ({{ facet.type }}){% endif %}{% if not loop.last %}, {% endif %}{% endfor %}
{% endif %}
diff --git a/datasette/views/base.py b/datasette/views/base.py
index 764ad7dd..db3956a7 100644
--- a/datasette/views/base.py
+++ b/datasette/views/base.py
@@ -363,7 +363,9 @@ class BaseView(RenderMixin):
SQL query took too long. The time limit is controlled by the
sql_time_limit_ms
configuration option.
- """, title="SQL Interrupted", status=400, messagge_is_html=True)
+
+ {}
+ """.format(e), title="SQL Interrupted", status=400, messagge_is_html=True)
except (sqlite3.OperationalError, InvalidSql) as e:
raise DatasetteError(str(e), title="Invalid SQL", status=400)
diff --git a/datasette/views/table.py b/datasette/views/table.py
index 3a26d247..09078880 100644
--- a/datasette/views/table.py
+++ b/datasette/views/table.py
@@ -5,6 +5,7 @@ import jinja2
from sanic.exceptions import NotFound
from sanic.request import RequestParameters
+from datasette.facets import load_facet_configs
from datasette.plugins import pm
from datasette.utils import (
CustomRow,
@@ -479,15 +480,12 @@ class TableView(RowTableShared):
)
# facets support
- # pylint: disable=no-member
- metadata_facets = table_metadata.get("facets", [])
- facets = metadata_facets[:]
- if request.args.get("_facet") and not self.ds.config("allow_facet"):
+ if not self.ds.config("allow_facet") and any(arg.startswith("_facet") for arg in request.args):
raise DatasetteError("_facet= is not allowed", status=400)
- try:
- facets.extend(request.args["_facet"])
- except KeyError:
- pass
+ facet_configs = load_facet_configs(request, table_metadata)
+ print("facet_configs", facet_configs)
+
+ # pylint: disable=no-member
facet_classes = list(
itertools.chain.from_iterable(pm.hook.register_facet_classes())
)
@@ -495,7 +493,7 @@ class TableView(RowTableShared):
facets_timed_out = []
facet_instances = []
for klass in facet_classes:
- facet_instances.append(klass(self.ds, request, database, table, configs=facets))
+ facet_instances.append(klass(self.ds, request, database, table, configs=facet_configs.get(klass.type)))
for facet in facet_instances:
instance_facet_results, instance_facets_timed_out = await facet.facet_results(
@@ -504,6 +502,7 @@ class TableView(RowTableShared):
facet_results.update(instance_facet_results)
facets_timed_out.extend(instance_facets_timed_out)
+ # Figure out columns and rows for the query
columns = [r[0] for r in results.description]
rows = list(results.rows)
@@ -653,7 +652,7 @@ class TableView(RowTableShared):
),
"extra_wheres_for_ui": extra_wheres_for_ui,
"form_hidden_args": form_hidden_args,
- "facet_hideable": lambda facet: facet not in metadata_facets,
+ "facet_hideable": lambda facet: facet not in [], # TODO: used to be metadata_facets fix this
"is_sortable": any(c["sortable"] for c in display_columns),
"path_with_replaced_args": path_with_replaced_args,
"path_with_removed_args": path_with_removed_args,