diff --git a/tests/python_client/cdc/perf/milvus_cdc_source_impact_test.py b/tests/python_client/cdc/perf/milvus_cdc_source_impact_test.py
new file mode 100644
index 0000000000..6e72812916
--- /dev/null
+++ b/tests/python_client/cdc/perf/milvus_cdc_source_impact_test.py
@@ -0,0 +1,609 @@
+import time
+import random
+import threading
+from concurrent.futures import ThreadPoolExecutor
+import json
+from datetime import datetime
+import requests
+from pymilvus import connections, Collection, DataType, FieldSchema, CollectionSchema, utility
+from loguru import logger
+import matplotlib.pyplot as plt
+import plotly.io as pio
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import numpy as np
+import pandas as pd
+import sys
+from collections import deque
+from statistics import mean
+logger.remove()
+logger.add(sink=sys.stdout, level="DEBUG")
+
+
+def create_interactive_plot(data, count_data, upstream_data):
+    # Convert timestamps to datetime
+    data['datetime'] = pd.to_datetime(data['timestamp'], unit='s')
+    count_data['datetime'] = pd.to_datetime(count_data['timestamp'], unit='s')
+    upstream_data['datetime'] = pd.to_datetime(upstream_data['timestamp'], unit='s')
+
+    # Create a figure with subplots
+    fig = make_subplots(rows=5, cols=1,
+                        subplot_titles=("Source and Target Collection Count over Time",
+                                        "Insert and Sync Throughput over Time",
+                                        "Latency over Time",
+                                        "Upstream Operations QPS",
+                                        "Upstream Operations Latency"),
+                        vertical_spacing=0.08)
+
+    # Plot 1: Source and Target Collection Count
+    fig.add_trace(go.Scatter(x=count_data['datetime'], y=count_data['source_count'],
+                             mode='lines', name='Source Count'),
+                  row=1, col=1)
+    fig.add_trace(go.Scatter(x=count_data['datetime'], y=count_data['target_count'],
+                             mode='lines', name='Target Count'),
+                  row=1, col=1)
+
+    # Plot 2: Insert and Sync Throughput
+    fig.add_trace(go.Scatter(x=data['datetime'], y=data['insert_throughput'],
+                             mode='lines', name='Insert Throughput'),
+                  row=2, col=1)
+    fig.add_trace(go.Scatter(x=data['datetime'], y=data['sync_throughput'],
+                             mode='lines', name='Sync Throughput'),
+                  row=2, col=1)
+
+    # Plot 3: Latency
+    fig.add_trace(go.Scatter(x=data['datetime'], y=data['real_time_latency'],
+                             mode='lines', name='Real-time Latency'),
+                  row=3, col=1)
+
+    # Plot 4: Upstream Operations QPS
+    fig.add_trace(go.Scatter(x=upstream_data['datetime'], y=upstream_data['delete_qps'],
+                             mode='lines', name='Delete QPS'),
+                  row=4, col=1)
+    fig.add_trace(go.Scatter(x=upstream_data['datetime'], y=upstream_data['query_qps'],
+                             mode='lines', name='Query QPS'),
+                  row=4, col=1)
+    fig.add_trace(go.Scatter(x=upstream_data['datetime'], y=upstream_data['search_qps'],
+                             mode='lines', name='Search QPS'),
+                  row=4, col=1)
+
+    # Plot 5: Upstream Operations Latency
+    fig.add_trace(go.Scatter(x=upstream_data['datetime'], y=upstream_data['delete_latency'],
+                             mode='lines', name='Delete Latency'),
+                  row=5, col=1)
+    fig.add_trace(go.Scatter(x=upstream_data['datetime'], y=upstream_data['query_latency'],
+                             mode='lines', name='Query Latency'),
+                  row=5, col=1)
+    fig.add_trace(go.Scatter(x=upstream_data['datetime'], y=upstream_data['search_latency'],
+                             mode='lines', name='Search Latency'),
+                  row=5, col=1)
+
+    # Update layout
+    fig.update_layout(height=2000, width=1000, title_text="Milvus CDC Performance Metrics")
+
+    # Update x-axes to show real datetime
+    for i in range(1, 6):
+        fig.update_xaxes(title_text="Time", row=i, col=1,
+                         tickformat="%Y-%m-%d %H:%M:%S")
+
+    fig.update_yaxes(title_text="Entity Count", row=1, col=1)
+    fig.update_yaxes(title_text="Throughput (entities/second)", row=2, col=1)
+    fig.update_yaxes(title_text="Latency (seconds)", row=3, col=1)
+    fig.update_yaxes(title_text="QPS", row=4, col=1)
+    fig.update_yaxes(title_text="Latency (seconds)", row=5, col=1)
+
+    return fig
+
+
+class MilvusCDCPerformance:
+    def __init__(self, source_alias, target_alias, cdc_host):
+        self.delete_entities_ts = []
+        self.cdc_paused = None
+        self.source_alias = source_alias
+        self.target_alias = target_alias
+        self.cdc_host = cdc_host
+        self.source_collection = None
+        self.target_collection = None
+        self.insert_count = 0
+        self.sync_count = 0
+        self.source_count = 0
+        self.target_count = 0
+        self.insert_lock = threading.Lock()
+        self.sync_lock = threading.Lock()
+        self.latest_insert_ts = 0
+        self.latest_query_ts = 0
+        self.stop_query = False
+        self.latencies = []
+        self.latest_insert_status = {
+            "latest_ts": 0,
+            "latest_count": 0
+        }
+        self.start_time = time.time()
+        self.last_report_time = time.time()
+        self.last_source_count = 0
+        self.last_target_count = 0
+
+        # New attributes for time series data
+        self.time_series_data = {
+            'timestamp': [],
+            'insert_throughput': [],
+            'sync_throughput': [],
+            'avg_latency': [],
+            'real_time_latency': []  # Add this line
+        }
+        self.count_series_data = {
+            'timestamp': [],
+            'source_count': [],
+            'target_count': []
+        }
+
+        self.delete_count = []
+        self.query_count = []
+        self.search_count = []
+        self.entities = []
+        self.delete_entities = []
+
+        self.upstream_data = {
+            'timestamp': [],
+            'delete_qps': [],
+            'query_qps': [],
+            'search_qps': [],
+            'delete_latency': [],
+            'query_latency': [],
+            'search_latency': []
+        }
+
+
+
+    def report_realtime_metrics(self):
+        current_time = time.time()
+        if self.last_report_time is None:
+            self.last_report_time = current_time
+            self.last_source_count = self.source_count
+            self.last_target_count = self.target_count
+            return
+
+        time_diff = current_time - self.last_report_time
+        insert_diff = self.source_count - self.last_source_count
+        sync_diff = self.target_count - self.last_target_count
+
+        insert_throughput = insert_diff / time_diff
+        sync_throughput = sync_diff / time_diff
+
+        avg_latency = sum(self.latencies[-100:]) / len(self.latencies[-100:]) if self.latencies else 0
+
+        logger.info(f"Real-time metrics:")
+        logger.info(f"  Insert Throughput: {insert_throughput:.2f} entities/second")
+        logger.info(f"  Sync Throughput: {sync_throughput:.2f} entities/second")
+        logger.info(f"  Avg Latency (last 100): {avg_latency:.2f} seconds")
+
+        # Store time series data
+        self.time_series_data['timestamp'].append(current_time)
+        self.time_series_data['insert_throughput'].append(insert_throughput)
+        self.time_series_data['sync_throughput'].append(sync_throughput)
+        self.time_series_data['avg_latency'].append(avg_latency)
+
+        self.last_report_time = current_time
+        self.last_source_count = self.source_count
+        self.last_target_count = self.target_count
+
+
+
+    def continuous_monitoring(self, interval=5):
+        while not self.stop_query:
+            real_time_latency = self.latencies[-1] if self.latencies else 0
+            self.time_series_data['real_time_latency'].append(real_time_latency)
+            self.report_realtime_metrics()
+
+            time.sleep(interval)
+    def continuous_monitoring_upstream(self, window_size=60):
+        while not self.stop_query:
+            current_time = time.time()
+            window_start = current_time - window_size
+
+            # Filter operations within the time window
+            delete_window = deque([op for op in self.delete_count if op[1] > window_start])
+            query_window = deque([op for op in self.query_count if op[1] > window_start])
+            search_window = deque([op for op in self.search_count if op[1] > window_start])
+
+            # Calculate QPS
+            delete_qps = len(delete_window) / window_size
+            query_qps = len(query_window) / window_size
+            search_qps = len(search_window) / window_size
+
+            # Calculate average latency
+            delete_latency = mean([op[0] for op in delete_window]) if delete_window else 0
+            query_latency = mean([op[0] for op in query_window]) if query_window else 0
+            search_latency = mean([op[0] for op in search_window]) if search_window else 0
+
+            # Store the data
+            self.upstream_data['timestamp'].append(current_time)
+            self.upstream_data['delete_qps'].append(delete_qps)
+            self.upstream_data['query_qps'].append(query_qps)
+            self.upstream_data['search_qps'].append(search_qps)
+            self.upstream_data['delete_latency'].append(delete_latency)
+            self.upstream_data['query_latency'].append(query_latency)
+            self.upstream_data['search_latency'].append(search_latency)
+
+            # Log the results
+            logger.info(f"Upstream metrics for the last {window_size} seconds:")
+            logger.info(f"Delete - QPS: {delete_qps:.2f}, Avg Latency: {delete_latency:.4f}s")
+            logger.info(f"Query  - QPS: {query_qps:.2f}, Avg Latency: {query_latency:.4f}s")
+            logger.info(f"Search - QPS: {search_qps:.2f}, Avg Latency: {search_latency:.4f}s")
+
+            # Update the operation counters to keep only the operations within the window
+            self.delete_count = list(delete_window)
+            self.query_count = list(query_window)
+            self.search_count = list(search_window)
+
+            # Wait for a short interval before the next analysis
+            time.sleep(5)
+
+    def plot_time_series_data(self):
+        df = pd.DataFrame(self.time_series_data)
+        count_df = pd.DataFrame(self.count_series_data)
+        upstream_df = pd.DataFrame(self.upstream_data)
+        fig = create_interactive_plot(df, count_df, upstream_df)
+        pio.write_html(fig, file='milvus_cdc_performance.html', auto_open=True)
+        logger.info("Interactive performance plot saved as 'milvus_cdc_performance.html'")
+
+    def list_cdc_tasks(self):
+        url = f"http://{self.cdc_host}:8444/cdc"
+        payload = json.dumps({"request_type": "list"})
+        response = requests.post(url, data=payload)
+        result = response.json()
+        logger.info(f"List CDC tasks response: {result}")
+        return result["data"]["tasks"]
+
+    def pause_cdc_tasks(self):
+        tasks = self.list_cdc_tasks()
+        for task in tasks:
+            task_id = task["task_id"]
+            url = f"http://{self.cdc_host}:8444/cdc"
+            payload = json.dumps({
+                "request_type": "pause",
+                "request_data": {"task_id": task_id}
+            })
+            response = requests.post(url, data=payload)
+            result = response.json()
+            logger.info(f"Pause CDC task {task_id} response: {result}")
+        self.cdc_paused = True
+        logger.info("All CDC tasks paused")
+
+    def resume_cdc_tasks(self):
+        tasks = self.list_cdc_tasks()
+        for task in tasks:
+            task_id = task["task_id"]
+            url = f"http://{self.cdc_host}:8444/cdc"
+            payload = json.dumps({
+                "request_type": "resume",
+                "request_data": {"task_id": task_id}
+            })
+            response = requests.post(url, data=payload)
+            result = response.json()
+            logger.info(f"Resume CDC task {task_id} response: {result}")
+        self.cdc_paused = False
+        logger.info("All CDC tasks resumed")
+
+    def pause_and_resume_cdc_tasks(self, duration):
+        time.sleep(duration / 3)
+        self.pause_cdc_tasks()
+        time.sleep(duration / 3)
+        self.resume_cdc_tasks()
+
+    def setup_collections(self):
+        self.resume_cdc_tasks()
+        fields = [
+            FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
+            FieldSchema(name="timestamp", dtype=DataType.INT64),
+            FieldSchema(name="tag", dtype=DataType.INT64),
+            FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=128)
+        ]
+        schema = CollectionSchema(fields, "Milvus CDC test collection")
+        c_name = "milvus_cdc_perf_test" + datetime.now().strftime("%Y%m%d%H%M%S")
+        # Create collections
+        self.source_collection = Collection(c_name, schema, using=self.source_alias, num_shards=4)
+        time.sleep(5)
+        self.target_collection = Collection(c_name, using=self.target_alias)
+        index_params = {
+            "index_type": "IVF_FLAT",
+            "metric_type": "L2",
+            "params": {"nlist": 1024}
+        }
+        self.source_collection.create_index("vector", index_params)
+        self.source_collection.load()
+        time.sleep(1)
+        logger.info(f"source collection: {self.source_collection.describe()}")
+        logger.info(f"target collection: {self.target_collection.describe()}")
+        # insert data for delete
+
+        for i in range(10):
+            entities = self.generate_data(10000, tag=1)
+            entities[0] = [int(time.time() * 1000000) - i * 1000 for _ in range(10000)]
+            self.source_collection.insert(entities)
+            self.delete_entities_ts.extend(entities[0])
+
+    def generate_data(self, num_entities, tag=0):
+        return [
+            [int(time.time() * 1000) for _ in range(num_entities)],  # timestamp
+            [tag for _ in range(num_entities)],  # tag
+            [[random.random() for _ in range(128)] for _ in range(num_entities)]  # vector
+        ]
+
+    def continuous_insert(self, duration, batch_size):
+        end_time = time.time() + duration
+        while time.time() < end_time:
+            entities = self.generate_data(batch_size)
+            self.entities = entities
+            self.source_collection.insert(entities)
+            with (self.insert_lock):
+                self.insert_count += batch_size
+                self.latest_insert_status = {
+                    "latest_ts": entities[0][-1],
+                    "latest_count": self.insert_count
+                }  # Update the latest insert timestamp
+                if random.random() < 0.1:
+                    logger.debug(
+                        f"insert_count: {self.insert_count}, latest_ts: {self.latest_insert_status['latest_ts']}")
+            time.sleep(0.01)  # Small delay to prevent overwhelming the system
+
+    def perform_delete(self):
+        while not self.stop_query:
+            try:
+                if len(self.entities) < 1:
+                    time.sleep(5)
+                    continue
+                ts = self.delete_entities_ts.pop()
+                expr = f"timestamp == {ts} and tag == 1"
+                t0 = time.time()
+                res = self.source_collection.delete(expr)
+                tt= time.time() - t0
+                logger.debug(f"Delete result:{res} with expr {expr}, cost {tt} seconds")
+                self.delete_count.append([tt, time.time()])
+            except Exception as e:
+                logger.debug(f"Delete failed: {e}")
+
+            time.sleep(0.01)
+
+    def perform_query(self):
+        while not self.stop_query:
+            try:
+                if len(self.entities) < 1:
+                    time.sleep(5)
+                    continue
+                mid_expr = self.entities[0][500] - 5000
+                max_expr = int(time.time()*1000)
+                expr = f"timestamp >= {mid_expr} && timestamp <= {max_expr}"
+
+                t0 = time.time()
+                res = self.source_collection.query(expr)
+                tt = time.time() - t0
+                logger.debug(f"Query result: {len(res)} with expr {expr},cost time: {tt} seconds")
+                self.query_count.append([tt, time.time()])
+            except Exception as e:
+                logger.debug(f"Query failed: {e}")
+
+    def perform_search(self):
+        while not self.stop_query:
+            try:
+                if len(self.entities) < 1:
+                    time.sleep(5)
+                    continue
+                vectors = self.entities[2][100:101]
+                t0 = time.time()
+                res = self.source_collection.search(
+                    data=vectors,
+                    anns_field="vector",
+                    param={"metric_type": "L2", "params": {"nprobe": 16}},
+                    limit=1
+                )
+                tt = time.time() - t0
+                logger.debug(f"Search result: {res}, cost time: {tt} seconds")
+                self.search_count.append([tt, time.time()])
+            except Exception as e:
+                logger.debug(f"Search failed: {e}")
+
+    def continuous_query(self):
+        while not self.stop_query:
+            with self.insert_lock:
+                latest_insert_ts = self.latest_insert_status["latest_ts"]
+                latest_insert_count = self.latest_insert_status["latest_count"]
+            if latest_insert_ts > self.latest_query_ts:
+                try:
+                    results = []
+                    t0 = time.time()
+                    while True and (time.time() - t0 < 10 * 60):
+                        try:
+                            results = self.target_collection.query(
+                                expr=f"timestamp == {latest_insert_ts}",
+                                output_fields=["timestamp"],
+                                limit=1
+                            )
+
+                        except Exception as e:
+                            logger.debug(f"Query failed: {e}")
+
+                        if len(results) > 0 and results[0]["timestamp"] == latest_insert_ts:
+                            logger.debug(
+                                f"query latest_insert_ts: {latest_insert_ts}, results: {results} query latency: {time.time() - t0} seconds")
+                            break
+                    tt = time.time() - t0
+                    # logger.info(f"start to query, latest_insert_ts: {latest_insert_ts}, results: {results}")
+                    if len(results) > 0 and results[0]["timestamp"] == latest_insert_ts:
+                        end_time = time.time()
+                        latency = end_time - (latest_insert_ts / 1000)  # Convert milliseconds to seconds
+                        with self.sync_lock:
+                            self.latest_query_ts = latest_insert_ts
+                            self.latencies.append(latency)
+                        logger.debug(
+                            f"query latest_insert_ts: {latest_insert_ts}, results: {results} query latency: {latency} seconds")
+                except Exception as e:
+                    logger.debug(f"Query failed: {e}")
+            time.sleep(0.01)  # Query interval
+
+    def continuous_count(self):
+
+        def count_target():
+            try:
+                t0 = time.time()
+                results = self.target_collection.query(
+                    expr="tag == 0",
+                    output_fields=["count(*)"],
+                    timeout=5
+                )
+                tt = time.time() - t0
+                self.target_count = results[0]['count(*)']
+            except Exception as e:
+                logger.error(f"Target count failed: {e}")
+                self.target_count = self.last_target_count
+
+        def count_source():
+            try:
+                t0 = time.time()
+                results = self.source_collection.query(
+                    expr="tag == 0",
+                    output_fields=["count(*)"],
+                    timeout=5
+                )
+                tt = time.time() - t0
+                self.source_count = results[0]['count(*)']
+            except Exception as e:
+                logger.error(f"Source count failed: {e}")
+                self.source_count = self.last_source_count
+
+        previous_count = self.target_collection.query(
+            expr="",
+            output_fields=["count(*)"],
+        )[0]['count(*)']
+        while not self.stop_query:
+            try:
+
+                thread1 = threading.Thread(target=count_target)
+                thread2 = threading.Thread(target=count_source)
+
+                thread1.start()
+                thread2.start()
+
+                thread1.join()
+                thread2.join()
+
+                progress = (self.target_count / self.source_count) * 100 if self.source_count > 0 else 0
+
+                self.sync_count = self.target_count - previous_count
+                self.count_series_data['timestamp'].append(time.time())
+                self.count_series_data['source_count'].append(self.source_count)
+                self.count_series_data['target_count'].append(self.target_count)
+                logger.debug(f"sync progress {self.target_count}/{self.source_count} {progress:.2f}%")
+            except Exception as e:
+                logger.error(f"Count failed: {e}")
+            time.sleep(0.01)  # Query interval
+
+    def measure_performance(self, duration, batch_size, concurrency):
+        self.insert_count = 0
+        self.sync_count = 0
+        self.latest_insert_ts = 0
+        self.latest_query_ts = int(time.time() * 1000)
+        self.latencies = []
+        self.stop_query = False
+
+        start_time = time.time()
+
+        # Start continuous query thread
+        query_thread = threading.Thread(target=self.continuous_query)
+        query_thread.start()
+        count_thread = threading.Thread(target=self.continuous_count)
+        count_thread.start()
+
+        cdc_thread = threading.Thread(target=self.pause_and_resume_cdc_tasks, args=(duration,))
+        cdc_thread.start()
+        upstream_op = [self.perform_query, self.perform_search, self.perform_delete]
+        upstream_op_task = []
+        for op in upstream_op:
+            task = threading.Thread(target=op)
+            task.start()
+            upstream_op_task.append(task)
+
+
+        monitor_thread = threading.Thread(target=self.continuous_monitoring)
+        monitor_thread.start()
+        monitoring_upstream_thread = threading.Thread(target=self.continuous_monitoring_upstream)
+        monitoring_upstream_thread.start()
+        # Start continuous insert threads
+        with ThreadPoolExecutor(max_workers=concurrency) as executor:
+            futures = [executor.submit(self.continuous_insert, duration, batch_size) for _ in range(concurrency)]
+
+        # Wait for all insert operations to complete
+        for future in futures:
+            future.result()
+
+        self.stop_query = True
+        query_thread.join()
+        count_thread.join()
+        cdc_thread.join()
+        for task in upstream_op_task:
+            task.join()
+        monitor_thread.join()
+
+        end_time = time.time()
+        total_time = end_time - start_time
+        insert_throughput = self.insert_count / total_time
+        sync_throughput = self.sync_count / total_time
+        avg_latency = sum(self.latencies) / len(self.latencies) if self.latencies else 0
+
+        logger.info(f"Test duration: {total_time:.2f} seconds")
+        logger.info(f"Total inserted: {self.insert_count}")
+        logger.info(f"Total synced: {self.sync_count}")
+        logger.info(f"Insert throughput: {insert_throughput:.2f} entities/second")
+        logger.info(f"Sync throughput: {sync_throughput:.2f} entities/second")
+        logger.info(f"Average latency: {avg_latency:.2f} seconds")
+        logger.info(f"Min latency: {min(self.latencies):.2f} seconds")
+        logger.info(f"Max latency: {max(self.latencies):.2f} seconds")
+        self.plot_time_series_data()
+        return total_time, self.insert_count, self.sync_count, insert_throughput, sync_throughput, avg_latency, min(
+            self.latencies), max(self.latencies)
+
+    def test_scalability(self, max_duration=600, batch_size=1000, max_concurrency=10):
+        results = []
+        for concurrency in range(10, max_concurrency + 1, 10):
+            self.resume_cdc_tasks()
+            logger.info(f"\nTesting with concurrency: {concurrency}")
+            total_time, insert_count, sync_count, insert_throughput, sync_throughput, avg_latency, min_latency, max_latency = self.measure_performance(
+                max_duration, batch_size, concurrency)
+            results.append((concurrency, total_time, insert_count, sync_count, insert_throughput, sync_throughput,
+                            avg_latency, min_latency, max_latency))
+
+        logger.info("\nScalability Test Results:")
+        for concurrency, total_time, insert_count, sync_count, insert_throughput, sync_throughput, avg_latency, min_latency, max_latency in results:
+            logger.info(f"Concurrency: {concurrency}")
+            logger.info(f"  Insert Throughput: {insert_throughput:.2f} entities/second")
+            logger.info(f"  Sync Throughput: {sync_throughput:.2f} entities/second")
+            logger.info(f"  Avg Latency: {avg_latency:.2f} seconds")
+            logger.info(f"  Min Latency: {min_latency:.2f} seconds")
+            logger.info(f"  Max Latency: {max_latency:.2f} seconds")
+
+        return results
+
+    def run_all_tests(self, duration=300, batch_size=1000, max_concurrency=10):
+        logger.info("Starting Milvus CDC Performance Tests")
+        self.setup_collections()
+        self.test_scalability(duration, batch_size, max_concurrency)
+        logger.info("Milvus CDC Performance Tests Completed")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description='cdc perf test')
+    parser.add_argument('--source_uri', type=str, default='http://10.104.18.188:19530', help='source uri')
+    parser.add_argument('--source_token', type=str, default='root:Milvus', help='source token')
+    parser.add_argument('--target_uri', type=str, default='http://10.104.4.87:19530', help='target uri')
+    parser.add_argument('--target_token', type=str, default='root:Milvus', help='target token')
+    parser.add_argument('--cdc_host', type=str, default='10.104.32.13', help='cdc host')
+    parser.add_argument('--test_duration', type=int, default=600, help='cdc test duration in seconds')
+
+    args = parser.parse_args()
+
+    connections.connect("source", uri=args.source_uri, token=args.source_token)
+    connections.connect("target", uri=args.target_uri, token=args.target_token)
+    cdc_test = MilvusCDCPerformance("source", "target", args.cdc_host)
+    cdc_test.run_all_tests(duration=args.test_duration, batch_size=1000, max_concurrency=10)