add db write to graph impl

.gitignore

@@ -1,4 +1,5 @@
 __pycache__
 .vscode
 checkpoints
-spark-warehouse
+spark-warehouse
+scratchpad.py

README.md

@@ -8,13 +8,17 @@ TODO
 
 - Python3
 - Apache spark 3.2 (https://spark.apache.org/downloads.html)
-- Cassandra DB (https://cassandra.apache.org/_/index.html, locally the docker build is recommended: https://hub.docker.com/_/cassandra)
+- Cassandra DB (https://cassandra.apache.org/\_/index.html, locally the docker build is recommended: https://hub.docker.com/\_/cassandra)
 
-For the graph implementation specifically you need to install `graphframes` manually since the official release is incompatible with `spark 3.x` (pull request pending). A prebuilt copy is supplied in the `spark-packages` directory. 
+For the graph implementation specifically you need to install `graphframes` manually from a third party since the official release is incompatible with `spark 3.x` ([pull request pending](https://github.com/graphframes/graphframes/pull/415)). A prebuilt copy is supplied in the `spark-packages` directory. 
 - graphframes (https://github.com/eejbyfeldt/graphframes/tree/spark-3.3)
 
 ## Setting up
 
-- Modify `settings.json` to reflect your setup. If you are running everything locally you can use `start_services.sh` to turn everything on in one swoop.
+- Modify `settings.json` to reflect your setup. If you are running everything locally you can use `start_services.sh` to turn everything on in one swoop. It might take a few minutes for Cassandra to become available.
-- Load the development database by running `python3 setup.py` from the project root.
+- Load the development database by running `python3 setup.py` from the project root. Per default this will move `small_test_data.csv` into the transactions table.
-- Start the spark workload by either running `submit.sh` (slow) or `submit_graph.sh` (faster)
+
+# Deploying:
+
+- Start the spark workload by either running `submit.sh` (slow) or `submit_graph.sh` (faster)
+- If you need to clean out the Database you can run `python3 clean.py`. Be wary that this wipes all data.

config/db/tables/clusters/CREATE.sql

@@ -1,5 +1,5 @@
 CREATE TABLE clusters(
     address TEXT,
-    parent TEXT,
+    id TEXT,
     PRIMARY KEY (address)
 );

src/spark/main.py

@@ -42,13 +42,13 @@ class Master:
         return self.spark \
             .read \
             .table(self.CLUSTERS_TABLE) \
-            .groupBy("parent") \
+            .groupBy("id") \
             .agg(F.collect_set('address').alias('addresses'))
 
     def insertNewCluster (self, addrs: Iterable[str], root: str | None = None) -> str:
         if(root == None):
             root = addrs[0]
-        df = self.spark.createDataFrame(map(lambda addr: (addr, root), addrs), schema=['address', 'parent'])
+        df = self.spark.createDataFrame(map(lambda addr: (addr, root), addrs), schema=['address', 'id'])
         df.writeTo(self.CLUSTERS_TABLE).append()
         return root
 
@@ -58,14 +58,14 @@ class Master:
             .zipWithIndex() \
             .toDF(["tx_group", "index"])
 
-    def rewrite_cluster_parent(self, cluster_roots: Iterable[str], new_cluster_root: str) -> None:
+    def rewrite_cluster_id(self, cluster_roots: Iterable[str], new_cluster_root: str) -> None:
         cluster_rewrite = self.spark \
             .table(self.CLUSTERS_TABLE) \
-            .where(F.col('parent').isin(cluster_roots)) \
+            .where(F.col('id').isin(cluster_roots)) \
             .select('address') \
             .rdd \
             .map(lambda addr: (addr['address'], new_cluster_root)) \
-            .toDF(['address', 'parent']) \
+            .toDF(['address', 'id']) \
         
         if(debug):
             print("REWRITE JOB")
@@ -73,24 +73,22 @@ class Master:
             print()
 
         cluster_rewrite.writeTo(self.CLUSTERS_TABLE).append()
-        
-        
 # end class Master
 
 
 """
     tuple structure:
-        Row => Row(parent=addr, addresses=list[addr] | the cluster
+        Row => Row(id=addr, addresses=list[addr] | the cluster
         Iterable[str] => list[addr] | the transaction addresses
 """
 def find(data: tuple[Row, Iterable[str]]) -> str | None:
     cluster = data[0]
     tx = data[1]
 
-    clusteraddresses = cluster['addresses'] + [cluster['parent']]
+    clusteraddresses = cluster['addresses'] + [cluster['id']]
 
     if any(x in tx for x in clusteraddresses):
-        return cluster['parent']
+        return cluster['id']
     else:
         return None
 
@@ -149,7 +147,7 @@ for i in range(0, tx_addr_groups.count()):
     elif(len(matched_roots) == 1):
         master.insertNewCluster(tx_addrs, matched_roots[0])
     else:
-        master.rewrite_cluster_parent(matched_roots[1:], matched_roots[0])
+        master.rewrite_cluster_id(matched_roots[1:], matched_roots[0])
         master.insertNewCluster(tx_addrs, matched_roots[0])
 
     if(debug):

src/spark/main_graphs.py

@@ -29,52 +29,55 @@ class Master:
             .config(f"spark.sql.catalog.{config['cassandra_catalog']}", "com.datastax.spark.connector.datasource.CassandraCatalog") \
             .getOrCreate()
 
-    def empty_dataframe(self, schema) -> DataFrame:
-        return self.spark.createDataFrame(self.spark.sparkContext.emptyRDD(), schema)
-
     def get_tx_dataframe(self) -> DataFrame:
         return self.spark.table(self.TX_TABLE)
 
     def get_cluster_dataframe(self) -> DataFrame:
         return self.spark.table(self.CLUSTERS_TABLE)
 
+    def write_connected_components_as_clusters(self, conn_comp: DataFrame) -> None:
+        conn_comp \
+            .withColumnRenamed('id', 'address') \
+            .withColumnRenamed('component', 'id') \
+            .writeTo(self.CLUSTERS_TABLE) \
+            .append()
+
 # end class Master
 
-
 master = Master(config)
-master.spark.sparkContext.setCheckpointDir(
+master.spark.sparkContext.setCheckpointDir('./checkpoints')  # spark is really adamant it needs this even if the algorithm is set to the non-checkpointed version
-    './checkpoints')  # spark is really adamant it needs this
 
-# Vertex DataFrame
+tx_df = master.get_tx_dataframe()
-transaction_as_vertices = master.get_tx_dataframe() \
+
+transaction_as_vertices =  tx_df \
     .select('address') \
     .withColumnRenamed('address', 'id') \
     .distinct()
 
 def explode_row(row: Row) -> List[Row]:
     addresses = row['addresses']
-    if(len(addresses) == 1):
-        return []
-
     return list(map(lambda addr: (addr, addresses[0]), addresses[1:]))
 
-
+transactions_as_edges = tx_df \
-tx_groups = master.get_tx_dataframe() \
     .groupBy("tx_id") \
-    .agg(F.collect_set('address').alias('addresses'))
+    .agg(F.collect_set('address').alias('addresses')) \
-
-transactions_as_edges = tx_groups \
     .rdd \
     .flatMap(explode_row) \
     .toDF(['src', 'dst'])
 
-
-# Create a GraphFrame
 g = GraphFrame(transaction_as_vertices, transactions_as_edges)
-res = g.connectedComponents().groupBy('component').agg(F.collect_list('id')).collect()
+components = g.connectedComponents(algorithm='graphframes')
 
-for row in res:
+master.write_connected_components_as_clusters(components)
-    print(sorted(row['collect_list(id)']))
+
+if(debug):
+    clusters = components \
+        .groupBy('component') \
+        .agg(F.collect_list('id')) \
+        .collect()
+
+    for cluster in clusters:
+        print(sorted(cluster['collect_list(id)'])) 
 
 end = time.time()
 print("ELAPSED TIME:", end-start)