non graph solution but extremely memory intensive
This commit is contained in:
nitowa
+136
-103
@@ -1,5 +1,6 @@
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from gc import collect
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import json
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from select import select
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from sqlite3 import Row
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from typing import Iterable, List
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@@ -14,6 +15,7 @@ start = time.time()
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config = json.load(open("./settings.json"))
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debug = config['debug']
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class Master:
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spark: SparkSession
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CLUSTERS_TABLE: str
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@@ -25,133 +27,164 @@ class Master:
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self.CLUSTERS_TABLE = f"{config['cassandra_catalog']}.{config['cassandra_keyspace']}.{config['clusters_table_name']}"
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self.TX_TABLE = f"{config['cassandra_catalog']}.{config['cassandra_keyspace']}.{config['tx_table_name']}"
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def makeSparkContext(self,config) -> SparkSession:
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def makeSparkContext(self, config) -> SparkSession:
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return SparkSession.builder \
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.appName('SparkCassandraApp') \
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.config(f"spark.sql.catalog.{config['cassandra_catalog']}", "com.datastax.spark.connector.datasource.CassandraCatalog") \
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.getOrCreate()
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.appName('SparkCassandraApp') \
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.config(f"spark.sql.catalog.{config['cassandra_catalog']}", "com.datastax.spark.connector.datasource.CassandraCatalog") \
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.getOrCreate()
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def group_tx_addrs(self) -> DataFrame:
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return self.spark \
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.read \
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.table(self.TX_TABLE) \
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.groupBy("tx_id") \
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.agg(F.collect_set('address').alias('addresses'))
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def get_tx_dataframe(self) -> DataFrame:
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return self.spark.table(self.TX_TABLE)
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def group_cluster_addrs(self) -> DataFrame:
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return self.spark \
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.read \
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.table(self.CLUSTERS_TABLE) \
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.groupBy("id") \
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.agg(F.collect_set('address').alias('addresses'))
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def union_single_col(self, df1: DataFrame, df2: DataFrame, column: str) -> DataFrame:
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return df1 \
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.select(column) \
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.union(df2.select(column))
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def insertNewCluster (self, addrs: Iterable[str], root: str | None = None) -> str:
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if(root == None):
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root = addrs[0]
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df = self.spark.createDataFrame(map(lambda addr: (addr, root), addrs), schema=['address', 'id'])
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df.writeTo(self.CLUSTERS_TABLE).append()
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return root
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def reduce_concat_array_column(self, df: DataFrame, column: str, distinct:bool = False) -> DataFrame:
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df = self.explode_array_col(df.select(column), column)
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return self.collect_col_to_array(df, column, distinct)
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def enumerate(self, data: DataFrame) -> DataFrame:
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return data \
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def collect_col_to_array(self, df: DataFrame, column: str, distinct: bool = False) -> DataFrame:
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if(distinct):
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return df.select(F.collect_set(column).alias(column))
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else:
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return df.select(F.collect_list(column).alias(column))
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def explode_array_col(self, df: DataFrame, column: str) -> DataFrame:
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return df \
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.rdd \
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.zipWithIndex() \
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.toDF(["tx_group", "index"])
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.flatMap(lambda row: list(map(lambda elem: (elem,), row[column]))) \
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.toDF([column])
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def rewrite_cluster_id(self, cluster_roots: Iterable[str], new_cluster_root: str) -> None:
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cluster_rewrite = self.spark \
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.table(self.CLUSTERS_TABLE) \
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.where(F.col('id').isin(cluster_roots)) \
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.select('address') \
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.rdd \
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.map(lambda addr: (addr['address'], new_cluster_root)) \
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.toDF(['address', 'id']) \
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def array_col_to_elements(self, df: DataFrame, column: str, distinct:bool = False) -> DataFrame:
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exploded = master.explode_array_col(
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df,
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column
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)
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if(debug):
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print("REWRITE JOB")
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cluster_rewrite.show(truncate=False, vertical=True)
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print()
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#this is likely redundant
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collected = master.collect_col_to_array(
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exploded,
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column,
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distinct
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)
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return self.explode_array_col(
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collected,
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column
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)
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cluster_rewrite.writeTo(self.CLUSTERS_TABLE).append()
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# end class Master
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"""
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tuple structure:
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Row => Row(id=addr, addresses=list[addr] | the cluster
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Iterable[str] => list[addr] | the transaction addresses
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"""
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def find(data: tuple[Row, Iterable[str]]) -> str | None:
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cluster = data[0]
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tx = data[1]
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clusteraddresses = cluster['addresses'] + [cluster['id']]
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if any(x in tx for x in clusteraddresses):
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return cluster['id']
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else:
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return None
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master = Master(config)
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tx_df = master.get_tx_dataframe()
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tx_addr_groups = master.group_tx_addrs()
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tx_groups_indexed = master.enumerate(tx_addr_groups).cache()
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for i in range(0, tx_addr_groups.count()):
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cluster_addr_groups = master.group_cluster_addrs()
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#Turn transactions into a list of ('id', [addr, addr, ...])
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tx_grouped = tx_df \
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.groupBy('tx_id') \
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.agg(F.collect_set('address').alias('addresses')) \
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.rdd \
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.zipWithIndex() \
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.toDF(['tx', 'index']) \
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.select(
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F.col('tx.tx_id').alias('tx_id'),
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F.col('tx.addresses').alias('addresses'),
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'index'
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) \
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.cache()
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if(debug):
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print("KNOWN CLUSTERS")
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cluster_addr_groups.show(truncate=True)
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print()
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tx_addrs: Iterable[str] = tx_groups_indexed \
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.where(tx_groups_indexed.index == i) \
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.select('tx_group') \
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.collect()[0]['tx_group']['addresses']
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# TODO: Load clusters from DB, check if any exist, if no make initial cluster, else proceed with loaded data
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if(debug):
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print("CURRENT TX")
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print(tx_addrs)
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print()
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# find initial cluster
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if (cluster_addr_groups.count() == 0):
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master.insertNewCluster(tx_addrs)
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continue
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# take the first tx
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tx_zero = tx_grouped \
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.select(tx_grouped.tx_id, tx_grouped.addresses) \
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.where(tx_grouped.index == 0)
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cluster_tx_mapping = cluster_addr_groups \
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# find txs with overlapping addresses
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overlapping_txs = tx_grouped \
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.where((tx_grouped.index != 0)) \
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.join(tx_zero.withColumnRenamed('addresses', 'tx_addresses')) \
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.select(
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tx_grouped.index,
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tx_grouped.addresses,
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F.arrays_overlap(tx_grouped.addresses, 'tx_addresses').alias('overlap')
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) \
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.where(F.col('overlap') == True) \
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# overlapped txs must not be considered anymore, so remove them candidate dataframe
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tx_grouped = tx_grouped \
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.join(overlapping_txs, 'index', 'leftanti') \
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.filter(tx_grouped.index != 0)
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# get the distinct addresses of all overlaps in a single array
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distinct_addresses = master.reduce_concat_array_column(
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master.union_single_col(
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overlapping_txs, tx_zero, column='addresses'
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),
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column='addresses',
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distinct=True,
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)
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#pick out a random representative for this cluster and add it to every address
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cluster = distinct_addresses \
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.rdd \
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.flatMap(lambda row: list(map(lambda addr: (addr, row['addresses'][0]), row['addresses']))) \
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.toDF(['address', 'id'])
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# done finding initial cluster
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#group cluster by representative and transform the result into a list of shape ('id', ['addr', 'addr', ...])
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clusters_grouped = cluster \
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.groupBy('id') \
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.agg(F.collect_list('address').alias('addresses'))
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def take_tx_and_cluster(txs: DataFrame, clusters: DataFrame):
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if (txs.count() == 0): # done!
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return clusters
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# take a random tx
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tx = txs \
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.select('*').limit(1)
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# find clusters with overlapping addresses from tx
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overlapping_clusters = clusters \
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.join(tx.withColumnRenamed('addresses', 'tx_addresses')) \
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.select(
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clusters.id,
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clusters.addresses,
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F.arrays_overlap(clusters.addresses,'tx_addresses').alias('overlap')
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) \
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.where(F.col('overlap') == True)
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#collect all addresses into single array field
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new_cluster_arr = master.reduce_concat_array_column(
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master.union_single_col(tx, overlapping_clusters, 'addresses'),
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column='addresses',
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distinct=True
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)
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#declare cluster representative
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new_cluster = new_cluster_arr \
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.rdd \
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.map(lambda cluster: (cluster, tx_addrs))
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.flatMap(lambda row: list(map(lambda addr: (addr, row['addresses'][0]), row['addresses']))) \
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.toDF(['address', 'id']) \
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.groupBy('id') \
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.agg(F.collect_list('address').alias('addresses'))
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if(debug):
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print("cluster_tx_mapping")
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cluster_tx_mapping \
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.toDF(['cluster', 'tx']) \
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.show(truncate=True)
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print()
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#start new round with txs minus the one just used, and updated clusters
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return take_tx_and_cluster(
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txs.join(tx, 'index', 'leftanti'),
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clusters.join(overlapping_clusters, 'id', 'leftanti').union(new_cluster)
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)
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matched_roots: "List[str]" = cluster_tx_mapping \
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.map(find) \
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.filter(lambda root: root != None) \
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.collect()
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if(debug):
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print("FOUND ROOTS")
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print(matched_roots)
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print()
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if(len(matched_roots) == 0):
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master.insertNewCluster(tx_addrs)
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elif(len(matched_roots) == 1):
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master.insertNewCluster(tx_addrs, matched_roots[0])
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else:
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master.rewrite_cluster_id(matched_roots[1:], matched_roots[0])
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master.insertNewCluster(tx_addrs, matched_roots[0])
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if(debug):
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print("======================================================================")
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take_tx_and_cluster(tx_grouped, clusters_grouped).show()
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end = time.time()
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print("ELAPSED TIME:", end-start)
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@@ -0,0 +1,157 @@
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from gc import collect
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import json
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from sqlite3 import Row
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from typing import Iterable, List
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from pyspark.sql import SparkSession, DataFrame, Row
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from pyspark.sql import functions as F
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import time
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start = time.time()
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config = json.load(open("./settings.json"))
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debug = config['debug']
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class Master:
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spark: SparkSession
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CLUSTERS_TABLE: str
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TX_TABLE: str
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def __init__(self, config):
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self.spark = self.makeSparkContext(config)
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self.config = config
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self.CLUSTERS_TABLE = f"{config['cassandra_catalog']}.{config['cassandra_keyspace']}.{config['clusters_table_name']}"
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self.TX_TABLE = f"{config['cassandra_catalog']}.{config['cassandra_keyspace']}.{config['tx_table_name']}"
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def makeSparkContext(self,config) -> SparkSession:
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return SparkSession.builder \
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.appName('SparkCassandraApp') \
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.config(f"spark.sql.catalog.{config['cassandra_catalog']}", "com.datastax.spark.connector.datasource.CassandraCatalog") \
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.getOrCreate()
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def group_tx_addrs(self) -> DataFrame:
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return self.spark \
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.read \
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.table(self.TX_TABLE) \
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.groupBy("tx_id") \
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.agg(F.collect_set('address').alias('addresses'))
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def group_cluster_addrs(self) -> DataFrame:
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return self.spark \
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.read \
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.table(self.CLUSTERS_TABLE) \
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.groupBy("id") \
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.agg(F.collect_set('address').alias('addresses'))
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def insertNewCluster (self, addrs: Iterable[str], root: str | None = None) -> str:
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if(root == None):
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root = addrs[0]
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df = self.spark.createDataFrame(map(lambda addr: (addr, root), addrs), schema=['address', 'id'])
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df.writeTo(self.CLUSTERS_TABLE).append()
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return root
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def enumerate(self, data: DataFrame) -> DataFrame:
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return data \
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.rdd \
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.zipWithIndex() \
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.toDF(["tx_group", "index"])
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def rewrite_cluster_id(self, cluster_roots: Iterable[str], new_cluster_root: str) -> None:
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cluster_rewrite = self.spark \
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.table(self.CLUSTERS_TABLE) \
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.where(F.col('id').isin(cluster_roots)) \
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.select('address') \
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.rdd \
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.map(lambda addr: (addr['address'], new_cluster_root)) \
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.toDF(['address', 'id']) \
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if(debug):
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print("REWRITE JOB")
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cluster_rewrite.show(truncate=False, vertical=True)
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print()
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cluster_rewrite.writeTo(self.CLUSTERS_TABLE).append()
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# end class Master
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"""
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tuple structure:
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Row => Row(id=addr, addresses=list[addr] | the cluster
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Iterable[str] => list[addr] | the transaction addresses
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"""
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def find(data: tuple[Row, Iterable[str]]) -> str | None:
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cluster = data[0]
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tx = data[1]
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clusteraddresses = cluster['addresses'] + [cluster['id']]
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if any(x in tx for x in clusteraddresses):
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return cluster['id']
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else:
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return None
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master = Master(config)
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tx_addr_groups = master.group_tx_addrs()
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tx_groups_indexed = master.enumerate(tx_addr_groups).cache()
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for i in range(0, tx_addr_groups.count()):
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cluster_addr_groups = master.group_cluster_addrs()
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if(debug):
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print("KNOWN CLUSTERS")
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cluster_addr_groups.show(truncate=True)
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print()
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tx_addrs: Iterable[str] = tx_groups_indexed \
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.where(tx_groups_indexed.index == i) \
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.select('tx_group') \
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.collect()[0]['tx_group']['addresses']
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if(debug):
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print("CURRENT TX")
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print(tx_addrs)
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print()
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if (cluster_addr_groups.count() == 0):
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master.insertNewCluster(tx_addrs)
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continue
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cluster_tx_mapping = cluster_addr_groups \
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.rdd \
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.map(lambda cluster: (cluster, tx_addrs))
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if(debug):
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print("cluster_tx_mapping")
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cluster_tx_mapping \
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.toDF(['cluster', 'tx']) \
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.show(truncate=True)
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print()
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matched_roots: "List[str]" = cluster_tx_mapping \
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.map(find) \
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.filter(lambda root: root != None) \
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.collect()
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if(debug):
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print("FOUND ROOTS")
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print(matched_roots)
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print()
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if(len(matched_roots) == 0):
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master.insertNewCluster(tx_addrs)
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elif(len(matched_roots) == 1):
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master.insertNewCluster(tx_addrs, matched_roots[0])
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else:
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master.rewrite_cluster_id(matched_roots[1:], matched_roots[0])
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master.insertNewCluster(tx_addrs, matched_roots[0])
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if(debug):
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print("======================================================================")
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end = time.time()
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print("ELAPSED TIME:", end-start)
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