Add temporary implementation for apps

backends/toncenter_cpp/apps_v2_projects.py

@@ -0,0 +1,77 @@
+import time
+from typing import Dict
+
+from models.backend import CalculationBackend
+from models.backends import BACKEND_REDOUBT
+from models.metric import MetricImpl, CalculationContext
+from models.results import ProjectStat, CalculationResults
+from models.scores import ScoreModel
+from models.season_config import SeasonConfig
+import psycopg2
+import json
+import psycopg2.extras
+from loguru import logger
+
+"""
+This backend will be used to aggegate score points for all users into project metrics
+"""
+
+class ToncenterCppAppsScores2Projects(CalculationBackend):
+    def __init__(self, connection):
+        CalculationBackend.__init__(self, "toncenter cpp indexer backend for App leaderboard, project version",
+                                    leaderboards=[SeasonConfig.APPS])
+        self.connection = connection
+
+    """
+    Update time for auxiliary table with messages
+    """
+    def get_update_time(self, config: SeasonConfig):
+        return 1726138800
+
+    def _do_calculate(self, config: SeasonConfig, dry_run: bool = False):
+
+        logger.info("Running toncenter backend for App leaderboard SQL generation, projects version")
+        PROJECTS_NAMES = []
+        context = CalculationContext(season=config, impl=BACKEND_REDOUBT)
+
+        for project in config.projects:
+            PROJECTS_NAMES.append(f"""
+            select '{project.name}' as project, '{project.url}' as url
+            """)
+        PROJECTS_NAMES = "\n union all \n".join(PROJECTS_NAMES)
+
+        SQL = f"""
+        with project_names as (
+        {PROJECTS_NAMES}
+        )
+        select project, url, 0 as total_uaw, 0 as enrolled_wallets, 0.0 as average_score
+        from project_names order by md5(project)
+        """
+        logger.info(f"Generated SQL: {SQL}")
+
+        results: Dict[str, ProjectStat] = {}
+
+        if dry_run:
+            logger.info("Running SQL query in dry_run mode")
+            with self.connection.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cursor:
+                cursor.execute(f"explain {SQL}")
+        else:
+            logger.info("Running SQL query in production mode")
+            with self.connection.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cursor:
+                cursor.execute(SQL)
+                for row in cursor.fetchall():
+                    results[row['project']] = ProjectStat(
+                        name=row['project'],
+                        metrics={
+                            ProjectStat.URL: row['url'],
+                            ProjectStat.APP_ONCHAIN_UAW: row['total_uaw'],
+                            ProjectStat.APP_ONCHAIN_ENROLLED_UAW: row['enrolled_wallets'],
+                            ProjectStat.APP_AVERAGE_SCORE: row['average_score']
+                        }
+                    )
+
+            self.connection.commit()
+            logger.info("Main query finished")
+
+        return CalculationResults(ranking=results.values(), build_time=1)
+