Current state: Under Discussion
ISSUE:
PRs:
Keywords: metric
Proxy
Released:
Proxy will provide an interface for users to show some important metrics information of Milvus.
When users use the Milvus, they may want to know exactly how Milvus is running. Now we already have integrated prometheus into our metrics system, users can use prometheus to monitor the Milvus. We also have project to interact with Milvus, though prometheus have already provided the metrics interface, it's not easy for milvus-insight
to use them and visualize them (If we do so, it seems that we are going to implement a front-end of parsing prometheus metrics). So as the access layer of Milvus, Proxy should provide an interface for users to show some important metrics information of Milvus.
This MEP will add a new interface below:
service MilvusService {
rpc GetMetrics(GetMetricsRequest) returns (GetMetricsResponse) {}
}
message GetMetricsRequest {
// request is the jsonic format string, in this way, we can also extend request easier,
string request = 1;
}
message GetMetricResponse {
common.Status status = 1;
// response is the jsonic format string, in this way, we can also extend response easier
string response = 2;
}
I will describe how this interface should be used later in Design Details.
As mentioned, users may want to know below metrics:
system version
nodes info
node name
hardware info
cpu info
memory info
capacity info
connection info (which node connect to which node)
created time
updated time
system configurations
hardware statistics(all and per node)
cpu info/usage
storage capacity/usage
node memory capacity/usage
loaded collections/partitions
name
loaded time
collections/partitions count
indexes count
QPS
latency
search/query logs
ip/time/query parameters
system Logs
system up/down logs
Most of metrics listed above can be reported directly, for example, hardware information, system configurations and etc.
For some meta-related metrics, such as loaded collections/partitions, Proxy can get these information from other components also.
There are two parts we need to discuss in detail:
Event Log Metrics
Milvus Connection Topology
A way to handle event log metrics such as dd/dm/dq operations is to use message stream. Other components as the producers will continously produce event logs to this stream, then Proxy as the consumer can read all event logs from stream and sort them with timestamp order. In this way, we need to consider the retention time of event log, as Milvus keeps running, event logs will become more and larger, Proxy shouldn't hold so many event logs, so a clean-up background thread is required to clean the logs peridically. Of course, we can also choose to persistent the event logs and then users can specific the timestamp section of event logs. There are many cloud-native logging systems, such as fluentd, etc.
I have two ideas about implementing this.
Plan 1 : Get topology information from other components. IndexCoord, QueryCoord, DataCoord should also provide an interface for Proxy to get metrics including node connection information. Since all coordinators in Milvus can manage their all related nodes, they can easily how many nodes connected to them and their nodes' hardware information.
Plan 2: Register the topology information into etcd. When nodes connect to coordinator, register their information too. Now we have an awsome encapsulation when nodes connect to others using Session
implemented by @qingxiang.chen. We can also encapsulate this operation to Session
.
GetMetrics
interface?The request
and response
are both of jsonic format string. Below are some examples, for some special response, I will explain how they are organized.
request:
{
"metric_type": "system_info"
}
response:
{ "nodes_info": [ { "identifier": 1, // unique in the list of nodes_info "name": "root coordinator", "hardware_info": { "ip": "192.168.1.1", "cpu_core_count": 2, "cpu_core_usage": "10%", "memory": "13124124", // in bytes "memory_usage": "234123", // in bytes "disk": "234123", // in bytes "disk_usage": "123123", // in bytes }, "system_info": { "system_version": "rc2 a3c662c7b", "deploy_mode": "cluster", }, "system_configurations": { "maxPartitionNum": 4096, "timeTickInterval": 200 }, "created_time": "2021-04-13 08:41:34.51+00", "updated_time": "2021-04-13 08:41:34.51+00", "type": "coordinator", "connected": [] }, { "identifier": 2, "name": "data coordinator", "hardware_info": { "ip": "192.168.1.1", "cpu_core_count": 2, "cpu_core_usage": "10%", "memory": "13124124", // in bytes "memory_usage": "234123", // in bytes "disk": "234123", // in bytes "disk_usage": "123123", // in bytes }, "system_info": { "system_version": "rc2 a3c662c7b", "deploy_mode": "cluster", }, "system_configurations": { "maxPartitionNum": 4096, "timeTickInterval": 200 }, "created_time": "2021-04-13 08:41:34.51+00", "updated_time": "2021-04-13 08:41:34.51+00", "type": "coordinator", "connected": [ { "parent": 1, "method": "manage" } ] }, { "identifier": 3, "name": "proxy", "hardware_info": { "ip": "192.168.1.1", "cpu_core_count": 2, "cpu_core_usage": "10%", "memory": "13124124", // in bytes "memory_usage": "234123", // in bytes "disk": "234123", // in bytes "disk_usage": "123123", // in bytes }, "system_info": { "system_version": "rc2 a3c662c7b", "deploy_mode": "cluster", }, "system_configurations": { "maxPartitionNum": 4096, "timeTickInterval": 200 }, "created_time": "2021-04-13 08:41:34.51+00", "updated_time": "2021-04-13 08:41:34.51+00", "type": "proxy", "connected": [ { "parent": 1, "method": "notification" }, { "parent": 2, "method": "notification" } ] }, { "identifier": 4, "name": "data node 1", "hardware_info": { "ip": "192.168.1.1", "cpu_core_count": 2, "cpu_core_usage": "10%", "memory": "13124124", // in bytes "memory_usage": "234123", // in bytes "disk": "234123", // in bytes "disk_usage": "123123", // in bytes }, "system_info": { "system_version": "rc2 a3c662c7b", "deploy_mode": "cluster", }, "system_configurations": { "maxPartitionNum": 4096, "timeTickInterval": 200 }, "created_time": "2021-04-13 08:41:34.51+00", "updated_time": "2021-04-13 08:41:34.51+00", "type": "data node", "connected": [ { "parent": 2, "method": "manage" } ] }, { "identifier": 5, "name": "data node 2", "hardware_info": { "ip": "192.168.1.1", "cpu_core_count": 2, "cpu_core_usage": "10%", "memory": "13124124", // in bytes "memory_usage": "234123", // in bytes "disk": "234123", // in bytes "disk_usage": "123123", // in bytes }, "system_info": { "system_version": "rc2 a3c662c7b", "deploy_mode": "cluster", }, "system_configurations": { "maxPartitionNum": 4096, "timeTickInterval": 200 }, "created_time": "2021-04-13 08:41:34.51+00", "updated_time": "2021-04-13 08:41:34.51+00", "type": "data node", "connected": [ { "parent": 2, "method": "manage" } ] } ] } |
In order to show the connection topology of Milvus, we have the nodes_info
in response. nodes_info
is a list and every item in list indicates a node in Milvus cluster. Every item has a identifier which is unique in nodes_info
. The identifier can be used in connected
content, for example, proxy has connected to root coordinator and data coordinator, so the connected
content is [1, 2]
, 1 is the identifier of root coordinator, 2 is the identifier of data coordinator.
{
"metric_type": "system_statistics"
}
response:
{
"hardware_statistics": [
{
"identifier": 1, // unique in the list of hardware_statistics
"name": "root coordinator",
"hardware_usage": {
"cpu": {
"type": "Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz",
"usage": 6
},
"memory": {
"total": 320000, // in mega bytes
"usage": 120000, // in mega bytes
}
}
}
// ...
],
"loaded_collections": [
{
"name": "coll1",
"loaded_time": "2021/07/05 11:13:44.372 +08:00",
"loaded_partitions": [
{
"name": "partition1",
"loaded_time": "2021/07/05 11:13:44.372 +08:00",
},
// ...
]
},
// ...
],
"collection_count": 3,
"partitions_count": [
{
"coll1": 2,
},
// ...
],
"indexes_count": [
{
"coll1": 2,
},
// ...
],
"qps": 10096,
"latency": 0.1
}
{
"metric_type": "system_log"
}
response:
{
"dd": [
"create collection 1 at ts1",
"create collection 2 at ts2"
],
"dm": [
"insert 20 records into collection 1",
"insert 30 records into collection 2"
],
"dq": [
"search on collection 1, nq: 10, topk = 5",
"search on collection 2, nq: 10, topk = 5"
]
}
test script written with pymilvus
:
#!/usr/bin/env python
import ujson
from pymilvus.grpc_gen import milvus_pb2 as milvus_types
ip = "127.0.0.1"
port = "19530"
if __name__ == "__main__":
client = Milvus(host=ip, port=port)
with client._connection() as handler:
system_info_req = ujson.dumps({"metric_type": "system_info"})
req = milvus_types.GetMetricsRequest(request=system_info_req)
resp = handler._stub.GetMetrics(req, wait_for_ready=True, timeout=None)
print(resp)
system_statistics_req = ujson.dumps({"metric_type": "system_statistics"})
req = milvus_types.GetMetricsRequest(request=system_statistics_req)
resp = handler._stub.GetMetrics(req, wait_for_ready=True, timeout=None)
print(resp)
system_logs_req = ujson.dumps({"metric_type": "system_logs"})
req = milvus_types.GetMetricsRequest(request=system_logs_req)
resp = handler._stub.GetMetrics(req, wait_for_ready=True, timeout=None)
print(resp)
client.close()