# Python-Client

MyScale ist mit ClickHouse kompatibel, daher können Sie den offiziellen ClickHouse-Client (opens new window) verwenden, um von Ihren Python-Anwendungen aus auf MyScale zuzugreifen.

Verwenden Sie den folgenden Befehl, um die erforderlichen Abhängigkeiten zu installieren:

pip install -U clickhouse-connect

# Verbindung herstellen

Um zu erfahren, wie Sie eine Verbindung zum Cluster herstellen, lesen Sie bitte den Abschnitt Verbindungsdetails.

# Tabelle erstellen

Verwenden Sie das Python SDK, um eine Tabelle mit den Spalten id, data, date und label zu erstellen. Beachten Sie, dass sich die SQL-Anweisungen zum Erstellen von Tabellen je nach Vektortyp unterscheiden. Derzeit unterstützt MyScale Gleitkommavektoren und Binärvektoren:

# Gleitkommavektoren

Wenn Ihre Vektordaten ein Float32-Array sind, können Sie der folgenden SQL-Anweisung folgen, um die Tabelle zu erstellen. Wir gehen davon aus, dass die anschließend in die Tabelle importierten Vektoren eine Länge von 128 haben.

# Tabelle erstellen, die 128-dimensionale Gleitkommavektoren enthält.
client.command("""
CREATE TABLE default.myscale_categorical_vector_search
(
    id    UInt32,
    data  Array(Float32),
    CONSTRAINT check_length CHECK length(data) = 128,
    date  Date,
    label Enum8('person' = 1, 'building' = 2, 'animal' = 3)
)
ORDER BY id""")
# Namen aller Tabellen in der aktuellen Datenbank abrufen und ausgeben.
res = client.query("SHOW TABLES").named_results()
print([r['name'] for r in res])

Ausgabe des Beispielcodes:

['myscale_categorical_vector_search']

# Binärvektoren

Wenn Ihre Vektordaten Binärdaten sind, können Sie wie folgt eine Tabelle namens myscale_categorical_binary_search erstellen, die die Spalten id, data, date und label enthält. Beachten Sie, dass die in die Tabelle importierten Binärdaten eine konsistente Länge haben sollten. Nehmen wir an, die Länge der zu importierenden Binärdaten beträgt 128, dann muss die Länge von FixedString auf 128/8=16 gesetzt werden.

# Tabelle zum Speichern von Binärdaten erstellen
client.command("""
CREATE TABLE default.myscale_categorical_binary_search
(
    id    UInt32,
    data  FixedString(16),
    date  Date,
    label Enum8('person' = 1, 'building' = 2, 'animal' = 3)
)
ORDER BY id""")
# Namen aller Tabellen in der aktuellen Datenbank abrufen und ausgeben.
res = client.query("SHOW TABLES").named_results()
print([r['name'] for r in res])

Ausgabe des Beispielcodes:

['myscale_categorical_binary_search']

# Daten importieren

# Gleitkommavektoren

Der Import von Gleitkommavektoren ist relativ direkt. Nehmen wir an, wir haben ein Pandas DataFrame mit folgenden Werten:

import pandas as pd
# Daten-Dictionary erstellen
data = {
    'id': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
    'data': [
        [0,0,0,1,8,7,3,2,5,0,0,3,5,7,11,31,13,0,0,0,0,29,106,107,13,0,0,0,1,61,70,42,0,0,0,0,1,23,28,16,63,4,0,0,0,6,83,81,117,86,25,15,17,50,84,117,31,23,18,35,97,117,49,24,68,27,0,0,0,4,29,71,81,47,13,10,32,87,117,117,45,76,40,22,60,70,41,9,7,21,29,39,53,21,4,1,55,72,3,0,0,0,0,9,65,117,73,37,28,23,17,34,11,11,27,61,64,25,4,0,42,13,1,1,1,14,10,6],
        [65,35,8,0,0,0,1,63,48,27,31,19,16,34,96,114,3,1,8,21,27,43,57,21,11,8,37,8,0,0,1,23,101,104,11,0,0,0,0,29,83,114,114,77,23,14,18,52,28,8,46,75,39,24,59,60,2,0,18,10,20,52,52,16,12,28,4,0,0,3,5,8,102,79,58,3,0,0,0,11,114,112,78,50,17,14,45,104,19,31,53,114,73,44,34,26,3,2,0,0,0,1,8,9,34,20,0,0,0,0,1,23,30,75,87,36,0,0,0,2,0,17,66,73,3,0,0,0],
        [0,0,0,0,0,0,4,1,15,0,0,0,0,0,10,49,27,0,0,0,0,29,113,114,9,0,0,0,3,69,71,42,14,0,0,0,0,1,56,79,63,2,0,0,0,38,118,77,118,60,8,8,18,48,59,104,27,16,7,13,80,118,34,21,118,47,4,0,0,1,32,99,61,40,31,57,46,118,118,61,80,64,16,21,20,33,23,27,6,22,16,14,51,33,0,0,76,40,8,0,2,14,42,94,19,42,57,67,23,34,22,10,9,52,15,21,5,1,3,3,1,38,12,5,18,1,0,0],
        [3,9,45,22,28,11,4,3,77,10,4,1,1,4,3,11,23,0,0,0,26,49,6,7,5,3,3,1,11,50,8,9,11,7,15,21,12,17,21,25,121,12,4,7,4,7,4,41,28,2,0,1,10,42,22,20,1,1,4,9,31,79,16,3,23,4,6,26,31,121,87,40,121,82,16,12,15,41,6,10,76,48,5,3,21,42,41,50,5,17,18,64,86,54,17,6,43,62,56,84,116,108,38,26,58,63,20,87,105,37,2,2,121,121,38,25,44,33,24,46,3,16,27,74,121,55,9,4],
        [6,4,3,7,80,122,62,19,2,0,0,0,32,60,10,19,4,0,0,0,0,10,69,66,0,0,0,0,8,58,49,5,5,31,59,67,122,37,1,2,50,1,0,16,99,48,3,27,122,38,6,7,11,31,87,122,9,8,6,23,122,122,69,21,0,11,31,55,28,0,0,0,61,4,0,37,43,2,0,15,122,122,55,32,6,1,0,12,5,22,52,122,122,9,2,0,2,0,0,5,28,20,2,2,19,3,0,2,12,12,3,16,25,18,34,35,5,4,1,13,21,2,22,51,9,20,57,59],
        [6,2,19,22,22,81,31,12,72,15,12,10,3,6,1,37,30,17,4,2,9,4,2,21,1,0,1,3,11,9,5,2,7,11,17,61,127,127,28,13,49,36,26,45,28,17,4,16,111,46,11,2,7,25,40,89,2,0,8,31,63,60,28,12,0,18,82,127,50,1,0,0,94,28,11,88,15,0,0,4,127,127,34,23,25,18,18,69,6,16,26,90,127,42,12,8,0,3,46,29,0,0,0,0,22,35,15,12,0,0,0,0,46,127,83,17,1,0,0,0,0,14,67,115,45,0,0,0],
        [19,35,5,6,40,23,18,4,21,109,120,23,5,12,24,5,0,5,87,108,47,14,32,8,0,0,0,27,36,30,43,0,29,12,10,15,6,7,17,12,34,9,14,65,20,23,28,14,120,34,14,14,9,34,120,120,7,6,7,27,56,120,120,23,9,5,4,7,2,6,46,13,29,5,5,32,12,20,99,19,120,120,107,38,13,7,24,36,6,24,120,120,55,26,4,3,5,1,0,0,1,5,19,18,2,2,0,1,18,12,30,7,0,5,33,29,66,50,26,2,0,0,49,45,12,28,10,0],
        [28,28,28,27,13,5,4,12,4,8,29,118,69,19,21,7,3,0,0,14,14,10,105,60,0,0,0,0,11,69,76,9,5,2,18,59,17,6,1,5,42,9,16,75,31,21,17,13,118,44,18,16,17,30,78,118,4,4,8,61,118,110,54,25,10,6,21,54,5,5,6,5,38,17,11,31,6,24,64,15,115,118,117,61,13,13,22,25,2,11,66,118,87,25,10,2,10,11,3,2,9,28,4,5,21,18,35,17,6,10,4,30,20,2,13,13,7,30,71,118,0,0,3,12,50,103,44,5],
        [41,38,21,17,42,71,60,50,11,1,2,11,109,115,8,4,27,8,5,22,11,9,8,14,20,10,4,33,12,7,4,1,18,115,95,42,17,1,0,0,19,6,46,115,91,16,0,7,66,7,4,15,12,32,91,109,12,3,1,8,21,115,96,17,1,51,78,14,0,0,0,0,50,40,62,53,0,0,0,3,115,115,40,12,6,13,25,65,7,30,51,65,110,92,25,9,0,1,13,0,0,0,0,0,4,22,11,1,0,0,0,0,13,115,48,1,0,0,0,0,0,36,102,63,11,0,0,0],
        [0,0,0,0,0,2,6,4,0,0,0,0,0,1,44,57,0,0,0,0,0,15,125,52,0,0,0,0,6,57,44,2,23,1,0,0,0,6,20,23,125,30,5,2,1,3,73,125,16,10,11,46,61,97,125,93,0,0,0,31,111,96,21,0,20,6,0,0,9,114,63,5,125,125,83,8,2,26,5,23,14,56,125,125,37,10,7,10,11,2,17,87,42,5,8,19,0,0,7,32,56,91,8,0,1,17,17,3,14,71,15,5,7,9,35,10,2,5,24,39,14,16,4,9,22,6,13,11]
    ],
    'date': ["2030-09-26", "1996-06-22", "1975-10-07", "2024-08-11", "1970-01-31", "2025-04-02", "2007-06-29", "1970-09-10", "2007-10-26", "1971-02-02"],
    'label': ["person", "building", "animal", "animal", "animal", "building", "animal", "building", "person", "building"]
}
# Erstellen des DataFrames
df = pd.DataFrame(data)

Daten können mit client.insert eingefügt werden:

# Anzahl der Zeilen in der Tabelle 'default.myscale_categorical_vector_search' abfragen
db_count_sql="SELECT count(*) FROM default.myscale_categorical_vector_search"
# Vor dem Einfügen von Daten die Anzahl der Zeilen in 'default.myscale_categorical_vector_search' abrufen und ausgeben
print(f"Vor dem Einfügen war db_count {client.command(db_count_sql)}")
# Daten in die Tabelle 'myscale_categorical_vector_search' einfügen
df_records = df.to_records(index=False)
df_records['date'] = pd.to_datetime(df_records['date'])
client.insert("default.myscale_categorical_vector_search", df_records.tolist(),
              column_names=df.columns.tolist())
# Nach dem Einfügen der Daten die Anzahl der Zeilen in 'default.myscale_categorical_vector_search' abrufen und ausgeben
print(f"Nach dem Einfügen ist db_count {client.command(db_count_sql)}")

Beispiel für die Ausführung des Codes:

vor dem Einfügen, db_count ist 0
nach dem Einfügen, db_count ist 10

# Binärvektoren

Der Import von Binärvektoren ist etwas komplexer. Nehmen wir an, wir haben ein Pandas DataFrame und müssen 10 Binärdatensätze importieren, deren Werte wie folgt aussehen:

import pandas as pd
# Daten-Dictionary erstellen
data = {
    'id': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
    'data': [
        [0,0,1,0,1,1,0,1,1,1,0,0,0,0,0,1,0,1,1,0,0,1,1,0,1,1,0,1,0,0,0,1,0,0,1,0,1,1,1,1,1,0,0,1,0,1,0,0,1,1,1,0,1,1,1,0,1,0,0,0,1,1,0,0,0,1,1,1,1,0,0,1,0,0,1,1,1,1,0,0,0,1,0,1,0,1,1,0,0,1,0,1,0,1,0,1,0,0,0,0,0,0,1,1,0,0,0,0,1,0,1,1,1,0,0,1,1,0,0,0,1,0,0,0,0,0,1,1],
        [1,1,1,0,0,1,0,1,1,0,0,1,0,1,1,1,0,1,1,1,0,0,1,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,1,1,1,0,1,1,1,0,1,1,1,0,1,1,0,1,1,1,0,1,0,1,0,1,0,0,1,1,1,1,0,0,0,0,0,1,0,1,0,0,1,0,1,1,1,1,0,1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,0,1,0,1,0,1,0,1,0,0,1,0,1,1,0,1],
        [0,1,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,0,0,1,0,0,1,0,1,1,0,1,0,0,0,1,0,1,0,0,0,0,1,0,0,0,1,0,1,1,0,0,0,0,1,0,0,1,1,0,0,1,0,0,0,1,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1,0,0,1,1,1,1,0,1,0,0,1,0,1,0,1,1,0,0,1,1,0,1,1,0,1,0,0,1,0,1,1,1,0,0,0,1,0,0,1,0,1,0,1,0,1,1,0,1],
        [1,1,1,1,1,1,1,0,1,0,0,1,0,1,0,0,0,0,1,1,1,0,0,1,1,0,1,0,1,1,1,0,1,0,1,1,0,0,0,0,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,0,0,0,1,0,1,1,0,0,1,1,1,1,0,1,1,1,0,0,0,1,0,0,0,1,0,1,1,0,1,0,0,0,0,1,1,0,1,1,1,0,1,1,0,1,0,0,0,0,1,1,0,0,0,0,0,1,0,0,1,1,1,1,0,0,0,1,0,1,1,1],
        [1,0,1,0,1,1,0,1,1,1,0,0,1,1,0,0,1,1,1,0,1,0,1,1,0,0,1,1,0,1,1,1,0,0,1,1,1,0,0,0,0,1,0,1,1,1,1,0,0,1,0,1,1,1,0,1,1,0,0,1,0,0,0,1,0,0,1,1,1,1,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1,0,0,0,0,0,0,1,1,0,0,0,1,0,1,0,1,0,0,0,0,0,1,1,0,0,0,1,1,1,1,0,0,1,0,0,0,0,0,0,1,1,0,0],
        [0,1,1,0,0,1,1,1,1,1,0,0,0,1,1,0,0,1,1,0,1,1,1,0,1,0,1,0,1,1,0,1,1,0,1,1,0,0,0,0,1,0,1,1,0,1,1,1,1,0,0,0,1,1,0,0,1,1,1,1,1,0,0,0,1,1,1,0,1,0,1,1,1,0,0,0,1,0,0,1,1,1,1,0,1,0,1,1,1,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,1,0,0,0,0,1,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,1],
        [1,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,1,1,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1,1,0,1,0,0,1,1,1,0,1,1,0,1,1,0,0,0,0,1,0,0,1,1,1,1,0,1,0,0,1,1,0,1,0,0,1,1,1,0,1,1,1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,1,0,1,1,1],
        [1,0,1,1,0,1,1,1,1,1,1,0,0,0,1,1,0,1,1,1,0,1,1,0,0,1,0,1,0,1,0,0,0,1,1,1,0,1,0,0,1,0,1,0,1,0,1,0,0,0,1,1,1,1,1,0,0,1,1,1,1,1,1,0,1,0,1,0,0,1,0,0,1,1,0,0,1,0,1,1,0,1,0,1,1,0,0,0,1,0,0,1,1,1,1,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,1,1,0],
        [1,1,0,1,0,1,1,1,1,0,0,0,0,0,1,1,1,1,1,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,1,1,1,1,0,0,0,1,0,1,1,1,1,1,0,1,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,1,1,1,0,1,0,1,0,1,1,1,0,1,1,0,1,1,0,1,1,0,0,0,0,0,1,0,1,1,0,1,1,0,1,0,0,1,0,0,0,1,1,0,0,1],
        [1,1,1,0,1,1,0,1,0,1,0,1,0,1,1,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,1,0,0,1,1,1,1,0,0,0,1,1,1,1,1,1,1,0,1,1,1,0,0,1,1,0,1,0,1,0,1,1,1,1,0,0,1,1,0,1,1,0,1,1,1,1,1,0,1,1,1,1,0,1,0,1,1,0,1,0,0,0,1,1,1,0,1,0,0,0,1,1,0,1,1]
    ],
    'date': ["2030-09-26", "1996-06-22", "1975-10-07", "2024-08-11", "1970-01-31", "2025-04-02", "2007-06-29", "1970-09-10", "2007-10-26", "1971-02-02"],
    'label': ["person", "building", "animal", "animal", "animal", "building", "animal", "building", "person", "building"]
}
# Erstellen des DataFrames
df = pd.DataFrame(data)

Daten können mit client.insert eingefügt werden:

# Anzahl der Zeilen in der Tabelle 'default.myscale_categorical_binary_search' abfragen
db_count_sql="SELECT count(*) FROM default.myscale_categorical_binary_search"
# Vor dem Einfügen von Daten die Anzahl der Zeilen in 'default.myscale_categorical_binary_search' abrufen und ausgeben
print(f"Vor dem Einfügen war db_count {client.command(db_count_sql)}")
# Daten in die Tabelle 'myscale_categorical_binary_search' einfügen
df_records = df.to_records(index=False)
# Datumsformat anpassen
df_records['date'] = pd.to_datetime(df_records['date'])
# Binärdaten in bytearray-Format konvertieren
for idx, vector in enumerate(df_records['data']):
    byte_array = bytearray()
    vector_str = "".join(str(it) for it in vector)
    for i in range(0, len(vector_str), 8):
        byte_value = int(vector_str[i:i + 8], 2)
        byte_array.append(byte_value)
    df_records['data'][idx] = byte_array
# Daten in die Datenbank importieren
client.insert("default.myscale_categorical_binary_search", df_records.tolist(),
              column_names=df.columns.tolist())
# Nach dem Einfügen der Daten die Anzahl der Zeilen in 'default.myscale_categorical_binary_search' abrufen und ausgeben
print(f"Nach dem Einfügen ist db_count {client.command(db_count_sql)}")

Beispiel für die Ausführung des Codes:

vor dem Einfügen, db_count ist 0
nach dem Einfügen, db_count ist 10

# Erstellen eines Vektorindexes

MyScale führt Indexerstellungsbefehle asynchron aus, was bedeutet, dass die Datenbank während der Indexerstellung nicht blockiert wird. Wenn die Tabelle jedoch sehr groß ist, kann die Indexerstellung trotzdem einige Zeit in Anspruch nehmen. Daher ist es wichtig, im Code zu überprüfen, ob der Index erfolgreich erstellt wurde. Hier ist ein Beispielcode, der zeigt, wie Sie überprüfen können, ob der Index erstellt wurde:

# Gleitkommavektoren

# 1. Index für vector<float32> erstellen
client.command("""
ALTER TABLE default.myscale_categorical_vector_search
    ADD VECTOR INDEX categorical_vector_idx data
    TYPE MSTG
""")
# 2. Systemtabelle 'vector_indices' abfragen, um den Indexerstellungsstatus zu überprüfen
get_index_status="SELECT status FROM system.vector_indices WHERE table='myscale_categorical_vector_search'"
# 3. Indexerstellungsstatus ausgeben, wenn der Index erfolgreich erstellt wurde, ist der Status 'Built'
print(f"Der Indexerstellungsstatus ist {client.command(get_index_status)}")

# Binärvektoren

# 1. Index für Binärdaten erstellen
client.command("""
ALTER TABLE default.myscale_categorical_binary_search
    ADD VECTOR INDEX categorical_binary_idx data
    TYPE BinaryMSTG('metric_type=Hamming')
""")
# 2. Systemtabelle 'vector_indices' abfragen, um den Indexerstellungsstatus zu überprüfen
get_index_status="SELECT status FROM system.vector_indices WHERE table='myscale_categorical_binary_search'"
# 3. Indexerstellungsstatus ausgeben, wenn der Index erfolgreich erstellt wurde, ist der Status 'Built'
print(f"Der Indexerstellungsstatus ist {client.command(get_index_status)}")

# Vektorsuche

In diesem Beispiel führen wir eine SQL-Abfrage aus, um id, date, label und die Distanz zwischen data und einem Beispielvektor mithilfe der distance-Funktion zu wählen. Die LIMIT 10 Klausel gibt an, dass die Funktion die 10 nächstgelegenen Vektoren zurückgeben soll.

# Gleitkommavektoren

 # Zufällige Zeile aus der Tabelle als Ziel auswählen
random_row = client.query("SELECT * FROM default.myscale_categorical_vector_search ORDER BY rand() LIMIT 1")
assert random_row.row_count == 1
target_row_id = random_row.first_item["id"]
target_row_label = random_row.first_item["label"]
target_row_date = random_row.first_item["date"]
target_row_data = random_row.first_item["data"]
print("Das aktuell ausgewählte Element hat id={}, label={}, date={}".format(target_row_id, target_row_label, target_row_date))
# Abfrageergebnisse abrufen
result = client.query(f"""
SELECT id, date, label, 
    distance(data, {target_row_data}) as dist FROM default.myscale_categorical_vector_search ORDER BY dist LIMIT 10
""")
# 3. Über die Zeilen des Abfrageergebnisses iterieren und 'id', 'date', 'label' und Distanzwert 'distance' jeder Zeile ausgeben
print("Top 10 Kandidaten:")
for row in result.named_results():
    print(row["id"], row["date"], row["label"], row["dist"])

Beispiel für die Ausführung des Codes:

Das aktuell ausgewählte Element hat id=3, label=animal, date=2024-08-11
Top 10 Kandidaten:
3   2024-08-11  animal      0.0
5   2025-04-02  building    211995.0
9   1971-02-02  building    214219.0
2   1975-10-07  animal      247505.0
0   2030-09-26  person      252941.0
1   1996-06-22  building    255835.0
7   1970-09-10  building    266691.0
4   1970-01-31  animal      276685.0
8   2007-10-26  person      284773.0
6   2007-06-29  animal      298423.0

# Binärvektoren

 # 1. Zufällige Zeile aus der Tabelle als Ziel auswählen
random_row = client.query("SELECT * FROM default.myscale_categorical_binary_search ORDER BY rand() LIMIT 1")
assert random_row.row_count == 1
target_row_id = random_row.first_item["id"]
target_row_label = random_row.first_item["label"]
target_row_date = random_row.first_item["date"]
target_row_data = random_row.first_item["data"]
vector_str = ""
for byte in target_row_data:
    binary_str = bin(byte)[2:].zfill(8)
    vector_str += binary_str
print("Das aktuell ausgewählte Element hat id={}, label={}, data={}, date={}".format(target_row_id, target_row_label, vector_str, target_row_date))
# 2. Abfrageergebnisse abrufen
result = client.query(f"""
SELECT id, date, label,
    distance(data, unbin('{vector_str}')) as dist FROM default.myscale_categorical_binary_search ORDER BY dist LIMIT 10
""")
# 3. Über die Zeilen des Abfrageergebnisses iterieren und 'id', 'date', 'label' und Distanzwert 'distance' jeder Zeile ausgeben
print("Top 10 Kandidaten:")
for row in result.named_results():
    print(row["id"], row["date"], row["label"], row["dist"])

Beispiel für die Ausführung des Codes:

Das aktuell ausgewählte Element hat id=1, label=building, data=11100101100101110111001100100100000100000110111011101110110111010101001111000001010010111101000111100000001111110101010100101101, date=1996-06-22
Top 10 Kandidaten:
1 1996-06-22 building 0.0
3 2024-08-11 animal 50.0
8 2007-10-26 person 50.0
9 1971-02-02 building 52.0
4 1970-01-31 animal 54.0
5 2025-04-02 building 54.0
6 2007-06-29 animal 54.0
2 1975-10-07 animal 56.0
7 1970-09-10 building 58.0
0 2030-09-26 person 65.0
Last Updated: Tue May 07 2024 02:28:12 GMT+0000