1.5.0 gremlin查询耗时比1.0.0高了20倍

[hexuxu110]

Bug Type (问题类型)

None

Before submit

• 我已经确认现有的 Issues 与 FAQ 中没有相同 / 重复问题 (I have confirmed and searched that there are no similar problems in the historical issue and documents)

Environment (环境信息)

• Server Version: 1.5.0
• Backend: hstore
• OS: xx CPUs, xx G RAM, Ubuntu 2x.x / CentOS 7.x
• Data Size: xx vertices, xx edges

Expected & Actual behavior (期望与实际表现)

1.0.0 gremlin查询耗时分析
{
"requestId": "2423af69-a8e9-4bcd-86a7-78530d59049c",
"status": {
"message": "",
"code": 200,
"attributes": {}
},
"result": {
"data": [
{
"dur": 24.312603,
"metrics": [
{
"dur": 0.101056,
"counts": {
"traverserCount": 1,
"elementCount": 1
},
"name": "HugeGraphStep(vertex,[650910907213576])",
"annotations": {
"percentDur": 0.4156527378002265
},
"id": "25.0.0()"
},
{
"dur": 0.419757,
"counts": {
"traverserCount": 55,
"elementCount": 55
},
"name": "HugeVertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"annotations": {
"percentDur": 1.7264996265517107
},
"id": "26.0.0()"
},
{
"dur": 13.776961,
"counts": {
"traverserCount": 19,
"elementCount": 19
},
"name": "TraversalFilterStep([EdgeOtherVertexStep, AndStep([[HugeVertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"annotations": {
"percentDur": 56.66592343074084
},
"id": "2.0.0()",
"metrics": [
{
"dur": 0.057391,
"counts": {
"traverserCount": 55,
"elementCount": 55
},
"name": "EdgeOtherVertexStep",
"id": "0.1.0(2.0.0())"
},
{
"dur": 13.388674,
"counts": {},
"name": "AndStep([[HugeVertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])",
"id": "2.1.0(2.0.0())",
"metrics": [
{
"dur": 7.167852,
"counts": {
"traverserCount": 96,
"elementCount": 96
},
"name": "HugeVertexStep(IN,edge)",
"id": "0.2.0(2.1.0(2.0.0()))"
},
{
"dur": 0.11395,
"counts": {
"traverserCount": 96,
"elementCount": 96
},
"name": "RangeGlobalStep(0,100)",
"id": "2.2.0(2.1.0(2.0.0()))"
},
{
"dur": 2.851844,
"counts": {
"traverserCount": 96,
"elementCount": 96
},
"name": "TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"id": "4.2.0(2.1.0(2.0.0()))",
"metrics": [
{
"dur": 0.048648,
"counts": {
"traverserCount": 96,
"elementCount": 96
},
"name": "EdgeOtherVertexStep",
"id": "0.3.0(4.2.0(2.1.0(2.0.0())))"
},
{
"dur": 2.521816,
"counts": {},
"name": "TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])",
"id": "2.3.0(4.2.0(2.1.0(2.0.0())))",
"metrics": [
{
"dur": 2.316642,
"counts": {
"traverserCount": 96,
"elementCount": 96
},
"name": "PropertiesStep([country_code],value)",
"id": "0.4.0(2.3.0(4.2.0(2.1.0(2.0.0()))))"
},
{
"dur": 0.059227,
"counts": {},
"name": "IsStep(eq(br))",
"id": "2.4.0(2.3.0(4.2.0(2.1.0(2.0.0()))))"
}
]
}
]
},
{
"dur": 0.221555,
"counts": {
"traverserCount": 81,
"elementCount": 81
},
"name": "RangeGlobalStep(0,2)",
"id": "6.2.0(2.1.0(2.0.0()))"
},
{
"dur": 0.089333,
"counts": {
"traverserCount": 55,
"elementCount": 55
},
"name": "CountGlobalStep",
"id": "8.2.0(2.1.0(2.0.0()))"
},
{
"dur": 0.053988,
"counts": {},
"name": "IsStep(gte(2))",
"id": "10.2.0(2.1.0(2.0.0()))"
},
{
"dur": 2.617619,
"counts": {},
"name": "OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])",
"id": "0.2.1(2.1.0(2.0.0()))",
"metrics": [
{
"dur": 2.327982,
"counts": {},
"name": "NotStep([PropertiesStep([status],property)])",
"id": "0.3.0(0.2.1(2.1.0(2.0.0())))",
"metrics": [
{
"dur": 2.280676,
"counts": {},
"name": "PropertiesStep([status],property)",
"id": "0.4.0(0.3.0(0.2.1(2.1.0(2.0.0()))))"
}
]
},
{
"dur": 0.01547,
"counts": {},
"name": "HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])",
"id": "0.3.1(0.2.1(2.1.0(2.0.0())))"
}
]
}
]
}
]
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{
"dur": 0.229685,
"counts": {
"traverserCount": 19,
"elementCount": 19
},
"name": "AggregateGlobalStep(res)",
"annotations": {
"percentDur": 0.9447157920523771
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"id": "3.0.0()"
},
{
"dur": 0.019327,
"counts": {
"traverserCount": 19,
"elementCount": 19
},
"name": "EdgeOtherVertexStep",
"annotations": {
"percentDur": 0.0794937506280179
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"id": "4.0.0()"
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{
"dur": 0.841858,
"counts": {
"traverserCount": 52,
"elementCount": 52
},
"name": "HugeVertexStep(IN,edge)",
"annotations": {
"percentDur": 3.4626403433643036
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"id": "27.0.0()"
},
{
"dur": 0.039229,
"counts": {
"traverserCount": 33,
"elementCount": 33
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"name": "WherePredicateStep(without([res]))",
"annotations": {
"percentDur": 0.16135252979699458
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"id": "6.0.0()"
},
{
"dur": 0.807154,
"counts": {
"traverserCount": 33,
"elementCount": 33
},
"name": "TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"annotations": {
"percentDur": 3.319899559911376
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"id": "7.0.0()",
"metrics": [
{
"dur": 0.016484,
"counts": {
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"elementCount": 33
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"name": "EdgeOtherVertexStep",
"id": "0.1.0(7.0.0())"
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{
"dur": 0.639737,
"counts": {},
"name": "TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])",
"id": "2.1.0(7.0.0())",
"metrics": [
{
"dur": 0.569226,
"counts": {
"traverserCount": 33,
"elementCount": 33
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"name": "PropertiesStep([country_code],value)",
"id": "0.2.0(2.1.0(7.0.0()))"
},
{
"dur": 0.016188,
"counts": {},
"name": "IsStep(eq(br))",
"id": "2.2.0(2.1.0(7.0.0()))"
}
]
}
]
},
{
"dur": 0.101168,
"counts": {
"traverserCount": 33,
"elementCount": 33
},
"name": "RangeGlobalStep(0,100)",
"annotations": {
"percentDur": 0.4161134042290741
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"id": "8.0.0()"
},
{
"dur": 0.048457,
"counts": {
"traverserCount": 33,
"elementCount": 33
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"name": "AggregateGlobalStep(res)",
"annotations": {
"percentDur": 0.19930815305954694
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"id": "9.0.0()"
},
{
"dur": 0.065663,
"counts": {
"traverserCount": 1,
"elementCount": 1
},
"name": "SideEffectCapStep([res])",
"annotations": {
"percentDur": 0.270078033191263
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"id": "10.0.0()"
},
{
"dur": 0.023558,
"counts": {
"traverserCount": 52,
"elementCount": 52
},
"name": "UnfoldStep",
"annotations": {
"percentDur": 0.0968962475963598
},
"id": "11.0.0()"
},
{
"dur": 0.050245,
"counts": {
"traverserCount": 104,
"elementCount": 104
},
"name": "EdgeVertexStep(BOTH)",
"annotations": {
"percentDur": 0.20666236354865006
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"id": "12.0.0()"
},
{
"dur": 0.042347,
"counts": {
"traverserCount": 29,
"elementCount": 29
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"name": "DedupGlobalStep",
"annotations": {
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"id": "13.0.0()"
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{
"dur": 0.017969,
"counts": {
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"elementCount": 29
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"name": "RangeGlobalStep(0,200)",
"annotations": {
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"id": "14.0.0()"
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{
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"counts": {
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"elementCount": 29
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"name": "TraversalMapStep([ProjectStep([id, cntMap],[id, [HugeVertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep(label)]])])",
"annotations": {
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},
"id": "15.0.0()",
"metrics": [
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"counts": {
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"name": "ProjectStep([id, cntMap],[id, [HugeVertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep(label)]])",
"id": "0.1.0(15.0.0())",
"metrics": [
{
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"counts": {
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"elementCount": 318
},
"name": "HugeVertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"id": "0.2.1(0.1.0(15.0.0()))"
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{
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"counts": {
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"name": "GroupCountStep(label)",
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},
{
"dur": 0.23048,
"counts": {
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"elementCount": 29
},
"name": "AggregateGlobalStep(cnt)",
"annotations": {
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"id": "16.0.0()"
},
{
"dur": 0.038722,
"counts": {
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"name": "SideEffectCapStep([res])",
"annotations": {
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"id": "17.0.0()"
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{
"dur": 0.023389,
"counts": {
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},
"name": "UnfoldStep",
"annotations": {
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"id": "18.0.0()"
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{
"dur": 0.32724,
"counts": {
"traverserCount": 52,
"elementCount": 52
},
"name": "TraversalMapStep([ProjectStep([id, label, create_time, update_time],[id, label, value(relation_create_time), value(relation_update_time)])])",
"annotations": {
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"id": "19.0.0()",
"metrics": [
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"counts": {
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"elementCount": 52
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"name": "ProjectStep([id, label, create_time, update_time],[id, label, value(relation_create_time), value(relation_update_time)])",
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},
{
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"counts": {
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"name": "AggregateGlobalStep(tree)",
"annotations": {
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"id": "20.0.0()"
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{
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"counts": {
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"name": "FoldStep",
"annotations": {
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"id": "21.0.0()"
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{
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"name": "ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])",
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"metrics": [
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"meta": {}
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}

Vertex/Edge example (问题点 / 边数据举例)

1.5.0 gremlin查询耗时分析
{
    "requestId": "c6855029-c521-4dd9-a7f4-046e7b023457",
    "status": {
        "message": "",
        "code": 200,
        "attributes": {}
    },
    "result": {
        "data": [
            {
                "dur": 363.87977,
                "metrics": [
                    {
                        "dur": 0.067726,
                        "counts": {
                            "traverserCount": 1,
                            "elementCount": 1
                        },
                        "name": "HugeGraphStep(vertex,[650910907213576])",
                        "annotations": {
                            "percentDur": 0.01861219160383662
                        },
                        "id": "25.0.0()"
                    },
                    {
                        "dur": 11.681859,
                        "counts": {
                            "traverserCount": 55,
                            "elementCount": 55
                        },
                        "name": "HugeVertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
                        "annotations": {
                            "percentDur": 3.210362312804584
                        },
                        "id": "26.0.0()"
                    },
                    {
                        "dur": 185.245014,
                        "counts": {
                            "traverserCount": 19,
                            "elementCount": 19
                        },
                        "name": "TraversalFilterStep([EdgeOtherVertexStep, AndStep([[HugeVertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
                        "annotations": {
                            "percentDur": 50.90830248683514
                        },
                        "id": "2.0.0()",
                        "metrics": [
                            {
                                "dur": 0.111199,
                                "counts": {
                                    "traverserCount": 55,
                                    "elementCount": 55
                                },
                                "name": "EdgeOtherVertexStep",
                                "id": "0.1.0(2.0.0())"
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                            {
                                "dur": 184.562016,
                                "counts": {},
                                "name": "AndStep([[HugeVertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])",
                                "id": "2.1.0(2.0.0())",
                                "metrics": [
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                                        "dur": 180.851272,
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                                        "id": "0.2.0(2.1.0(2.0.0()))"
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                                        "dur": 0.280725,
                                        "counts": {
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                                        "name": "RangeGlobalStep(0,100)",
                                        "id": "2.2.0(2.1.0(2.0.0()))"
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                                    {
                                        "dur": 1.602583,
                                        "counts": {
                                            "traverserCount": 96,
                                            "elementCount": 96
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                                        "name": "TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
                                        "id": "4.2.0(2.1.0(2.0.0()))",
                                        "metrics": [
                                            {
                                                "dur": 0.121201,
                                                "counts": {
                                                    "traverserCount": 96,
                                                    "elementCount": 96
                                                },
                                                "name": "EdgeOtherVertexStep",
                                                "id": "0.3.0(4.2.0(2.1.0(2.0.0())))"
                                            },
                                            {
                                                "dur": 0.972873,
                                                "counts": {},
                                                "name": "TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])",
                                                "id": "2.3.0(4.2.0(2.1.0(2.0.0())))",
                                                "metrics": [
                                                    {
                                                        "dur": 0.531377,
                                                        "counts": {
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                                                            "elementCount": 96
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                                                        "name": "PropertiesStep([country_code],value)",
                                                        "id": "0.4.0(2.3.0(4.2.0(2.1.0(2.0.0()))))"
                                                    },
                                                    {
                                                        "dur": 0.127585,
                                                        "counts": {},
                                                        "name": "IsStep(eq(br))",
                                                        "id": "2.4.0(2.3.0(4.2.0(2.1.0(2.0.0()))))"
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                                    {
                                        "dur": 0.387148,
                                        "counts": {
                                            "traverserCount": 81,
                                            "elementCount": 81
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                                        "name": "RangeGlobalStep(0,2)",
                                        "id": "6.2.0(2.1.0(2.0.0()))"
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                                    {
                                        "dur": 0.200268,
                                        "counts": {
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                                            "elementCount": 55
                                        },
                                        "name": "CountGlobalStep",
                                        "id": "8.2.0(2.1.0(2.0.0()))"
                                    },
                                    {
                                        "dur": 0.120295,
                                        "counts": {},
                                        "name": "IsStep(gte(2))",
                                        "id": "10.2.0(2.1.0(2.0.0()))"
                                    },
                                    {
                                        "dur": 0.661526,
                                        "counts": {},
                                        "name": "OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])",
                                        "id": "0.2.1(2.1.0(2.0.0()))",
                                        "metrics": [
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                                                "dur": 0.256986,
                                                "counts": {},
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                                                "id": "0.3.0(0.2.1(2.1.0(2.0.0())))",
                                                "metrics": [
                                                    {
                                                        "dur": 0.189884,
                                                        "counts": {},
                                                        "name": "PropertiesStep([status],property)",
                                                        "id": "0.4.0(0.3.0(0.2.1(2.1.0(2.0.0()))))"
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                                                ]
                                            },
                                            {
                                                "dur": 0.217571,
                                                "counts": {},
                                                "name": "HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])",
                                                "id": "0.3.1(0.2.1(2.1.0(2.0.0())))"
                                            }
                                        ]
                                    }
                                ]
                            }
                        ]
                    },
                    {
                        "dur": 0.431963,
                        "counts": {
                            "traverserCount": 19,
                            "elementCount": 19
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                        "name": "AggregateGlobalStep(res)",
                        "annotations": {
                            "percentDur": 0.1187103641403313
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                        "id": "3.0.0()"
                    },
                    {
                        "dur": 0.064853,
                        "counts": {
                            "traverserCount": 19,
                            "elementCount": 19
                        },
                        "name": "EdgeOtherVertexStep",
                        "annotations": {
                            "percentDur": 0.01782264510060562
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                        "id": "4.0.0()"
                    },
                    {
                        "dur": 34.713822,
                        "counts": {
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}

Schema [VertexLabel, EdgeLabel, IndexLabel] (元数据结构)

[dosubot]

你反馈的 1.5.0 版本 Gremlin 查询比 1.0.0 慢 20 倍，结合代码和社区资料，主要可能原因有：

1. 后端配置和集群模式影响：1.5.0 默认推荐集群（hstore）模式，哪怕单机部署也会引入分布式调度、锁和网络通信等额外开销，实际测试中多层查询性能下降 10~20 倍是已知现象（如这里）。建议优先确认是否为集群模式，单机建议用单节点模式或优化集群配置。

2. JVM 和缓存参数：1.5.0 建议 JVM 堆内存至少 16GB，生产建议 32GB（-Xmx32g -Xms32g），并调大 vertex/edge cache 容量和过期时间，避免频繁访问后端存储，提升查询性能（参考）。

3. 索引覆盖与查询优化：确保所有 has/hasLabel 等查询条件都被二级索引或范围索引覆盖，避免全表扫描。复杂多层 out()/in() 查询建议加 limit 或分页，减少一次性拉取过多数据导致 OOM 或超时（参考）。

4. hstore 后端实现变化：1.5.0 hstore 后端对查询做了更多分片、锁和 session 管理，部分查询会被拆分成多个子查询，且读写锁竞争、批量参数、线程池等配置如果不合理会极大影响性能。建议对比 1.0.0 和 1.5.0 的 hstore 配置，适当调大 batch size、线程池、rocksdb 缓存等参数（参考）。

5. 代码层面：HugeVertexStep、TraversalFilterStep 等核心遍历步骤在 1.5.0 可能有过滤、迭代策略、索引使用等实现细节变化，复杂过滤条件会导致大量数据在内存中二次过滤，极大拖慢查询（参考）。

排查建议：

• 优先检查 JVM、缓存、hstore 相关配置，尽量与 1.0.0 保持一致。
• 检查 schema 索引覆盖，避免全表扫描。
• 用 Arthas 等工具分析慢查询瓶颈是在计算层还是存储层。
• 如为集群模式，建议单机测试单节点模式对比性能。
• 查询语句建议加 limit、分页，减少多层 out()/in() 链式遍历。

如需进一步定位，建议补充具体 schema、数据量、硬件配置和 hstore 详细参数。

To reply, just mention @dosu.

---

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[hexuxu110]

数据量:50亿边 50亿点
硬件配置 300core 600g内存
查询gremlin语句:
g_rocksdb.traversal().V(a_uid).outE(edge_type as String[]).where(otherV().and(inE().limit(a_degree).where(otherV().where(values('country_code').is(eq(a_country)))).limit(2).count().is(gte(2)),or(where(hasNot('status')),has('status',without(dirty_status as String[]))))).aggregate('res').otherV().inE().where(without('res')).where(otherV().where(values('country_code').is(eq(a_country)))).limit(a_limit).aggregate('res').cap('res').unfold().bothV().dedup().limit(a_limit2).map(project('id','cntMap').by(id()).by(bothE(edge_type as String[]).groupCount().by(label()))).aggregate('cnt').cap('res').unfold().map(project('id','label','create_time','update_time').by(id()).by(label()).by(values('relation_create_time')).by(values('relation_update_time'))).aggregate('tree').fold().project('tree','cnt').by(cap('tree')).by(cap('cnt'))
@dosu

[dosubot]

你的查询在 HugeGraph 1.5.0 下性能大幅下降，主要瓶颈集中在多层 HugeVertexStep、TraversalFilterStep、AndStep 等遍历和过滤环节。代码实现上，这些步骤会先构造后端查询（如 GraphTransaction.constructEdgesQuery），再用 FilterIterator 在内存中做二次过滤，复杂条件和多层 inE/outE 会导致大量数据在 JVM 内部处理，极易拖慢查询，尤其是在 50 亿点/边的大规模数据下，分布式锁、session 管理和后端通信开销也显著增加代码分析。

优化建议：

• JVM 堆建议 32GB 以上，memory.mode 设为 off-heap，vertex/edge cache_capacity、cache_expire 调大，避免频繁后端 IO，提升缓存命中率配置建议。
• 检查所有 where/has/filter 条件是否有二级或范围索引覆盖，避免全表扫描。
• 查询语句建议加 limit、分页，减少一次性拉取和多层 outE/inE 链式遍历。
• hstore 后端参数（分片数、线程池、batch size、rocksdb 缓存等）需根据硬件和数据量调优，避免锁竞争和 session 过多。
• 用 Arthas 等工具定位慢查询瓶颈是在计算层还是存储层，关注 FilterIterator、validateVertex/Edge 等环节的内存和 CPU 占用。

如需更细致定位，建议补充 schema 索引结构和 hstore 详细配置。

To reply, just mention @dosu.

---

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[hexuxu110]

@dosu
我的jvm 堆大小是64G

[dosubot]

你的 JVM 堆内存 64GB 已经非常充足，不会是 1.5.0 查询变慢的根本原因。当前 HugeGraph 1.5.0 查询慢，主要还是和 hstore/集群模式的分布式调度、锁、后端通信、索引覆盖、缓存参数、RocksDB 配置等有关。建议重点排查：

1. 检查所有 where/has/filter 条件是否有二级或范围索引覆盖，避免全表扫描，schema 设计要覆盖高频查询路径 参考。
2. 查询语句尽量减少多层 out()/in() 链式遍历，优先用 has() 精准过滤起点，并加 limit、分页，避免一次性拉取过多数据导致 OOM 或超时 参考。
3. cache 参数（vertex/edge cache_capacity、cache_expire）可适当调大，提升缓存命中率，减少后端 IO 参考。
4. hstore/RocksDB 后端建议关注 max_background_jobs、write_buffer_size、block_cache_capacity、optimize_mode 等参数，充分利用你的内存和 CPU 资源 参考。
5. 高并发场景下建议限制单次查询返回数据量，合理设置 memory.mode=off-heap，避免 JVM 堆和 native 内存争抢 参考。
6. 用 Arthas 等工具分析慢查询瓶颈是在计算层还是存储层，定位是 FilterIterator、validateVertex/Edge 还是后端 IO 参考。

如需更细致定位，建议补充 schema 索引结构和 hstore 详细配置。

To reply, just mention @dosu.

---

^{How did I do? Good | Irrelevant | Incorrect | Verbose | Hallucination | Report 🐛 | Other}    

[hexuxu110]

hstore配置:

pdserver:
address: a:8686,b:8686,c:8686,d:8686,e:8686,g:8686,h:8686,m:8686

management:
metrics:
export:
prometheus:
enabled: true
endpoints:
web:
exposure:
include: "*"

grpc:
host: 10.15.87.21
port: 8500
netty-server:
max-inbound-message-size: 1000MB
raft:
disruptorBufferSize: 1024
address: 10.15.87.21:8510
max-log-file-size: 600000000000
snapshotInterval: 1800
rpc-timeout: 30000
maxEntriesSize: 512
maxBodySize: 2097152
snapshotLogIndexMargin: 2048

server:
port: 8520

app:
data-path: ./storage

spring:
application:
name: store-node-grpc-server
profiles:
active: default
include: pd

logging:
config: 'file:./conf/log4j2.xml'
level:
root: info

rocksdb:
total_memory_size: 68719476736
block_cache_capacity: 34359738368
write_buffer_ratio: 0.5
max_background_jobs: 32
max_subcompactions: 16
bloom_filter_bits_per_key: 10
bloom_filter_block_based_mode: true
bloom_filter_whole_key_filtering: true
optimize_filters_for_hits: false
compression: lz4
compression_per_level: none,none,lz4,lz4,lz4,lz4,lz4
bottommost_compression: lz4
max_file_opening_threads: 64
delayed_write_rate: 134217728

schema：

{"propertykeys":[{"id":7,"name":"id_type","data_type":"TEXT","cardinality":"SINGLE","aggregate_type":"NONE","write_type":"OLTP","properties":[],"status":"CREATED","user_data":{"~create_time":"2023-04-24 10:36:26.579"}},{"id":6,"name":"device_type","data_type":"TEXT","cardinality":"SET","aggregate_type":"NONE","write_type":"OLTP","properties":[],"status":"CREATED","user_data":{"~create_time":"2023-04-24 10:36:26.576"}},{"id":9,"name":"card_issuing_country","data_type":"TEXT","cardinality":"SINGLE","aggregate_type":"NONE","write_type":"OLTP","properties":[],"status":"CREATED","user_data":{"~create_time":"2023-04-24 10:36:26.584"}},{"id":8,"name":"card_type","data_type":"TEXT","cardinality":"SINGLE","aggregate_type":"NONE","write_type":"OLTP","properties":[],"status":"CREATED","user_data":{"~create_time":"2023-04-24 10:36:26.581"}},{"id":3,"name":"create_time","data_type":"INT","cardinality":"SINGLE","aggregate_type":"NONE","write_type":"OLTP","properties":[],"status":"CREATED","user_data":{"~create_time":"2023-04-24 10:36:26.568"}},{"id":2,"name":"role","data_type":"BYTE","cardinality":"SINGLE","aggregate_type":"NONE","write_type":"OLTP","properties":[],"status":"CREATED","user_data":{"~create_time":"2023-04-24 10:36:26.565"}},{"id":5,"name":"country_code","data_type":"TEXT","cardinality":"SINGLE","aggregate_type":"NONE","write_type":"OLTP","properties":[],"status":"CREATED","user_data":{"~create_time":"2023-04-24 10:36:26.574"}},{"id":4,"name":"update_time","data_type":"INT","cardinality":"SINGLE","aggregate_type":"NONE","write_type":"OLTP","properties":[],"status":"CREATED","user_data":{"~create_time":"2023-04-24 10:36:26.571"}},{"id":1,"name":"origins","data_type":"BYTE","cardinality":"SET","aggregate_type":"NONE","write_type":"OLTP","properties":[],"status":"CREATED","user_data":{"~create_time":"2023-04-24 10:36:26.555"}},{"id":11,"name":"status","data_type":"TEXT","cardinality":"SINGLE","aggregate_type":"NONE","write_type":"OLTP","properties":[],"status":"CREATED","user_data":{"~create_time":"2023-04-24 10:36:26.589"}},{"id":12,"name":"relation_create_time","data_type":"LONG","cardinality":"SINGLE","aggregate_type":"NONE","write_type":"OLTP","properties":[],"status":"CREATED","user_data":{"~create_time":"2023-04-24 10:36:26.592"}},{"id":10,"name":"open_type","data_type":"TEXT","cardinality":"SINGLE","aggregate_type":"NONE","write_type":"OLTP","properties":[],"status":"CREATED","user_data":{"~create_time":"2023-04-24 10:36:26.587"}},{"id":13,"name":"relation_update_time","data_type":"LONG","cardinality":"SINGLE","aggregate_type":"NONE","write_type":"OLTP","properties":[],"status":"CREATED","user_data":{"~create_time":"2023-04-24 10:36:26.595"}},{"id":14,"name":"multi_scene_mark","data_type":"INT","cardinality":"SINGLE","aggregate_type":"NONE","write_type":"OLTP","properties":[],"status":"CREATED","user_data":{"~create_time":1745842776582}}],"vertexlabels":[{"id":7,"name":"id_card","id_strategy":"CUSTOMIZE_STRING","primary_keys":[],"nullable_keys":["id_type","status"],"index_labels":[],"properties":["origins","create_time","update_time","id_type","status"],"status":"CREATED","ttl":0,"enable_label_index":false,"user_data":{"~create_time":"2023-04-24 10:36:26.622","~style":"{"icon":"","color":"#fe5b5d","size":"SMALL","display_fields":["~id"],"join_symbols":["-"]}"}},{"id":8,"name":"car","id_strategy":"CUSTOMIZE_STRING","primary_keys":[],"nullable_keys":["status"],"index_labels":[],"properties":["origins","create_time","update_time","status"],"status":"CREATED","ttl":0,"enable_label_index":false,"user_data":{"~create_time":"2023-04-24 10:36:26.625","~style":"{"icon":"","color":"#4d8dda","size":"SMALL","display_fields":["~id"],"join_symbols":["-"]}"}},{"id":9,"name":"device_fp","id_strategy":"CUSTOMIZE_STRING","primary_keys":[],"nullable_keys":["origins","create_time","update_time","device_type","status"],"index_labels":[],"properties":["origins","create_time","update_time","device_type","status"],"status":"CREATED","ttl":0,"enable_label_index":false,"user_data":{"~create_time":1749107618387,"~style":"{"icon":"","color":"#569380","size":"SMALL","display_fields":["~id"],"join_symbols":["-"]}"}},{"id":1,"name":"user","id_strategy":"CUSTOMIZE_STRING","primary_keys":[],"nullable_keys":["role","country_code","status"],"index_labels":[],"properties":["origins","role","create_time","update_time","country_code","status"],"status":"CREATED","ttl":0,"enable_label_index":false,"user_data":{"~create_time":"2023-04-24 10:36:26.604","~style":"{"icon":"","color":"#5c73e6","size":"NORMAL","display_fields":["~id"],"join_symbols":["-"]}"}},{"id":2,"name":"phone","id_strategy":"CUSTOMIZE_STRING","primary_keys":[],"nullable_keys":["status"],"index_labels":[],"properties":["origins","create_time","update_time","status"],"status":"CREATED","ttl":0,"enable_label_index":false,"user_data":{"~create_time":"2023-04-24 10:36:26.608","~style":"{"icon":"","color":"#f2ca00","size":"SMALL","display_fields":["~id"],"join_symbols":["-"]}"}},{"id":3,"name":"email","id_strategy":"CUSTOMIZE_STRING","primary_keys":[],"nullable_keys":["status"],"index_labels":[],"properties":["origins","create_time","update_time","status"],"status":"CREATED","ttl":0,"enable_label_index":false,"user_data":{"~create_time":"2023-04-24 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10:36:26.642","~style":"{"color":"#5C73E6","with_arrow":true,"line_type":"SOLID","thickness":"NORMAL","display_fields":["~id"],"join_symbols":["-"]}"}},{"id":5,"name":"user_id_card","edgelabel_type":"NORMAL","source_label":"user","target_label":"id_card","links":[{"user":"id_card"}],"frequency":"SINGLE","sort_keys":[],"nullable_keys":["multi_scene_mark"],"index_labels":[],"properties":["origins","relation_create_time","relation_update_time","multi_scene_mark"],"status":"CREATED","ttl":0,"enable_label_index":false,"user_data":{"~create_time":"2023-04-24 10:36:26.645","~style":"{"color":"#5C73E6","with_arrow":true,"line_type":"SOLID","thickness":"NORMAL","display_fields":["~id"],"join_symbols":["-"]}"}},{"id":6,"name":"user_bank_card","edgelabel_type":"NORMAL","source_label":"user","target_label":"bank_card","links":[{"user":"bank_card"}],"frequency":"SINGLE","sort_keys":[],"nullable_keys":["multi_scene_mark"],"index_labels":[],"properties":["origins","relation_create_time","relation_update_time","multi_scene_mark"],"status":"CREATED","ttl":0,"enable_label_index":false,"user_data":{"~create_time":"2023-04-24 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10:36:26.650","~style":"{"color":"#5C73E6","with_arrow":true,"line_type":"SOLID","thickness":"NORMAL","display_fields":["~id"],"join_symbols":["-"]}"}},{"id":8,"name":"user_device_finger","edgelabel_type":"NORMAL","source_label":"user","target_label":"device_fp","links":[{"user":"device_fp"}],"frequency":"SINGLE","sort_keys":[],"nullable_keys":["origins","relation_create_time","relation_update_time","multi_scene_mark"],"index_labels":[],"properties":["origins","relation_create_time","relation_update_time","multi_scene_mark"],"status":"CREATED","ttl":0,"enable_label_index":false,"user_data":{"~create_time":1749108217218,"~style":"{"color":"#5c73e6","with_arrow":true,"line_type":"SOLID","thickness":"NORMAL","display_fields":["~id"],"join_symbols":["-"]}"}}],"indexlabels":[]}

@dosu

[dosubot]

你的 schema 没有任何二级索引（indexlabels），所有 where/has/filter 查询都会全表扫描，这是 1.5.0 查询极慢的核心原因，尤其在 50 亿点/边规模下，链式 outE/inE 查询会导致每层都拉全量数据在内存中过滤，性能会指数级下降。HugeGraph 1.5.0 的 HugeVertexStep、TraversalFilterStep、AndStep 等遍历步骤，底层会先构造后端查询，再用 FilterIterator 在 JVM 内存中做二次过滤，没有索引时会极大拖慢查询，且分布式锁和 session 管理进一步放大了延迟代码分析。

必须优化 schema：

• 给所有高频查询属性（如 country_code、status、multi_scene_mark 等）补充二级索引（secondary index），如：

  schema.indexLabel("userByCountryCode").onV("user").by("country_code").secondary().create()
  schema.indexLabel("userByStatus").onV("user").by("status").secondary().create()
  schema.indexLabel("edgeByMultiSceneMark").onE("user_phone").by("multi_scene_mark").secondary().create()

• 索引生效后，所有 where/has/filter 查询会走索引，避免全表扫描，性能提升数十倍官方建议。

配置建议：

• JVM 堆 64GB 已足够，memory.mode 建议设为 off-heap，避免堆内存和 RocksDB native 内存争抢配置说明。
• vertex.cache_capacity、edge.cache_capacity 建议大幅调高（如 1 亿/5000 万），cache_expire 适当延长，充分利用 600GB 内存，减少后端 IO。
• rocksdb.total_memory_size、block_cache_capacity、max_background_jobs、max_file_opening_threads 可继续调大，充分利用 300 核心和 600GB 内存性能调优。
• 分片数建议与节点数和数据量匹配，避免单分片过大或 leader 迁移频繁。

查询语句建议：

• 多层 outE/inE 建议加 limit、分页，减少一次性拉取数据量，避免 OOM。
• 用 has() 精准过滤起点，减少链式遍历范围。

验证与排查：

• 索引创建后，重启服务并确认 schema 已生效。
• 用 Arthas 或 REST API 检查慢查询瓶颈是在存储层还是计算层，关注 FilterIterator、validateVertex/Edge 的 CPU/内存占用。
• 检查集群健康和分片分布，避免部分节点/分片负载过高。

优先级建议：

1. 立即补充所有高频属性的二级索引，避免全表扫描。
2. 调大 cache、rocksdb、线程池等参数，充分利用硬件。
3. 优化查询语句结构，加 limit、分页，减少链式遍历。
4. 用监控工具定位瓶颈，持续调优。

如需具体索引创建语句或参数建议，可补充说明。

To reply, just mention @dosu.

---

^{How did I do? Good | Irrelevant | Incorrect | Verbose | Hallucination | Report 🐛 | Other}    

[hexuxu110]

@dosu
我本就是主键id查询 不需要二级索引 这是我的执行计划 {
"requestId": "adf4464d-f2a6-4f49-942a-21442622b0c4",
"status": {
"message": "",
"code": 200,
"attributes": {}
},
"result": {
"data": [
{
"original": [
"GraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[TraversalFilterStep([NotStep([PropertiesStep([status],value)])])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[[IdStep], [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep([LabelStep])]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[[IdStep], [LabelStep], [PropertiesStep([relation_create_time],value)], [PropertiesStep([relation_update_time],value)]])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"intermediate": [
{
"traversal": [
"GraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[TraversalFilterStep([NotStep([PropertiesStep([status],value)])])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[[IdStep], [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep([LabelStep])]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[[IdStep], [LabelStep], [PropertiesStep([relation_create_time],value)], [PropertiesStep([relation_update_time],value)]])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "ConnectiveStrategy",
"category": "DecorationStrategy"
},
{
"traversal": [
"GraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[TraversalFilterStep([NotStep([PropertiesStep([status],value)])])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[[IdStep], [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep([LabelStep])]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[[IdStep], [LabelStep], [PropertiesStep([relation_create_time],value)], [PropertiesStep([relation_update_time],value)]])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "IdentityRemovalStrategy",
"category": "OptimizationStrategy"
},
{
"traversal": [
"GraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[TraversalFilterStep([NotStep([PropertiesStep([status],value)])])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[[IdStep], [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep([LabelStep])]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[[IdStep], [LabelStep], [PropertiesStep([relation_create_time],value)], [PropertiesStep([relation_update_time],value)]])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "MatchPredicateStrategy",
"category": "OptimizationStrategy"
},
{
"traversal": [
"GraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[TraversalFilterStep([NotStep([PropertiesStep([status],value)])])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[[IdStep], [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep([LabelStep])]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[[IdStep], [LabelStep], [PropertiesStep([relation_create_time],value)], [PropertiesStep([relation_update_time],value)]])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "FilterRankingStrategy",
"category": "OptimizationStrategy"
},
{
"traversal": [
"GraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],value)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[[IdStep], [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep([LabelStep])]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[[IdStep], [LabelStep], [PropertiesStep([relation_create_time],value)], [PropertiesStep([relation_update_time],value)]])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "InlineFilterStrategy",
"category": "OptimizationStrategy"
},
{
"traversal": [
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"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],value)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
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"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[[IdStep], [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep([LabelStep])]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[[IdStep], [LabelStep], [PropertiesStep([relation_create_time],value)], [PropertiesStep([relation_update_time],value)]])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "IncidentToAdjacentStrategy",
"category": "OptimizationStrategy"
},
{
"traversal": [
"GraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[[IdStep], [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep([LabelStep])]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[[IdStep], [LabelStep], [PropertiesStep([relation_create_time],value)], [PropertiesStep([relation_update_time],value)]])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "AdjacentToIncidentStrategy",
"category": "OptimizationStrategy"
},
{
"traversal": [
"GraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[[IdStep], [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep([LabelStep])]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[[IdStep], [LabelStep], [PropertiesStep([relation_create_time],value)], [PropertiesStep([relation_update_time],value)]])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "CountStrategy",
"category": "OptimizationStrategy"
},
{
"traversal": [
"GraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[[IdStep], [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep([LabelStep])]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[[IdStep], [LabelStep], [PropertiesStep([relation_create_time],value)], [PropertiesStep([relation_update_time],value)]])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "RepeatUnrollStrategy",
"category": "OptimizationStrategy"
},
{
"traversal": [
"GraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[[IdStep], [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep([LabelStep])]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[[IdStep], [LabelStep], [PropertiesStep([relation_create_time],value)], [PropertiesStep([relation_update_time],value)]])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "PathRetractionStrategy",
"category": "OptimizationStrategy"
},
{
"traversal": [
"GraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[id, [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep(label)]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[id, label, value(relation_create_time), value(relation_update_time)])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "ByModulatorOptimizationStrategy",
"category": "OptimizationStrategy"
},
{
"traversal": [
"GraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[id, [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep(label)]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[id, label, value(relation_create_time), value(relation_update_time)])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "EarlyLimitStrategy",
"category": "OptimizationStrategy"
},
{
"traversal": [
"GraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[id, [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep(label)]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[id, label, value(relation_create_time), value(relation_update_time)])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "LazyBarrierStrategy",
"category": "OptimizationStrategy"
},
{
"traversal": [
"GraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[id, [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep(label)]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[id, label, value(relation_create_time), value(relation_update_time)])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "HugePrimaryKeyStrategy",
"category": "ProviderOptimizationStrategy"
},
{
"traversal": [
"HugeGraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[id, [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep(label)]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[id, label, value(relation_create_time), value(relation_update_time)])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "HugeGraphStepStrategy",
"category": "ProviderOptimizationStrategy"
},
{
"traversal": [
"HugeGraphStep(vertex,[650910907213576])",
"VertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[VertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"VertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[id, [VertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep(label)]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[id, label, value(relation_create_time), value(relation_update_time)])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "HugeCountStepStrategy",
"category": "ProviderOptimizationStrategy"
},
{
"traversal": [
"HugeGraphStep(vertex,[650910907213576])",
"HugeVertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[HugeVertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"HugeVertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[id, [HugeVertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep(label)]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[id, label, value(relation_create_time), value(relation_update_time)])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "HugeVertexStepStrategy",
"category": "ProviderOptimizationStrategy"
},
{
"traversal": [
"HugeGraphStep(vertex,[650910907213576])",
"HugeVertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[HugeVertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"HugeVertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[id, [HugeVertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep(label)]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[id, label, value(relation_create_time), value(relation_update_time)])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "ProfileStrategy",
"category": "FinalizationStrategy"
},
{
"traversal": [
"HugeGraphStep(vertex,[650910907213576])",
"HugeVertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[HugeVertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"HugeVertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[id, [HugeVertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep(label)]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[id, label, value(relation_create_time), value(relation_update_time)])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
],
"strategy": "StandardVerificationStrategy",
"category": "VerificationStrategy"
}
],
"final": [
"HugeGraphStep(vertex,[650910907213576])",
"HugeVertexStep(OUT,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge)",
"TraversalFilterStep([EdgeOtherVertexStep, AndStep([[HugeVertexStep(IN,edge), RangeGlobalStep(0,100), TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])]), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gte(2))], [OrStep([[NotStep([PropertiesStep([status],property)])], [HasStep([status.without([dirty_node(link_user>1500), dirty_node(link_diff_country_user_and_link_user>1000), dirty_node(orings=7_label=device_link_user>=100_reg_city>=5)])])]])]])])",
"AggregateGlobalStep(res)",
"EdgeOtherVertexStep",
"HugeVertexStep(IN,edge)",
"WherePredicateStep(without([res]))",
"TraversalFilterStep([EdgeOtherVertexStep, TraversalFilterStep([PropertiesStep([country_code],value), IsStep(eq(br))])])",
"RangeGlobalStep(0,100)",
"AggregateGlobalStep(res)",
"SideEffectCapStep([res])",
"UnfoldStep",
"EdgeVertexStep(BOTH)",
"DedupGlobalStep",
"RangeGlobalStep(0,200)",
"TraversalMapStep([ProjectStep([id, cntMap],[id, [HugeVertexStep(BOTH,[user_id_card, user_phone, user_bank_card, user_email, user_device, user_openid],edge), GroupCountStep(label)]])])",
"AggregateGlobalStep(cnt)",
"SideEffectCapStep([res])",
"UnfoldStep",
"TraversalMapStep([ProjectStep([id, label, create_time, update_time],[id, label, value(relation_create_time), value(relation_update_time)])])",
"AggregateGlobalStep(tree)",
"FoldStep",
"ProjectStep([tree, cnt],[[SideEffectCapStep([tree])], [SideEffectCapStep([cnt])]])"
]
}
],
"meta": {}
}
}

[dosubot]

你的确是主键 id 起点查询，GraphStep(vertex,[id]) 这一步不需要二级索引，性能也不会有瓶颈。但你的 Gremlin 查询后续包含了多层 outE/inE/bothE 遍历和复杂 where/filter 条件（如 country_code、status 等），这些步骤在 HugeGraph 1.5.0（hstore 架构）下如果没有二级索引，依然会导致全表扫描和大量数据在 JVM 内存中过滤，极大拖慢查询。

代码实现上，HugeVertexStep、TraversalFilterStep、AndStep 等遍历步骤会先构造后端查询（GraphTransaction.constructEdgesQuery），再用 FilterIterator 在内存中过滤属性。没有索引时，每层都要拉取大量边/点数据，尤其在 50 亿点/边规模下，性能会指数级下降，硬件再强也无法弥补索引缺失带来的全表扫描和内存压力（详见代码分析）。

社区和官方 issue 也明确指出，所有 where/has/filter 查询条件都必须有二级或范围索引覆盖，才能避免全表扫描，提升多层遍历和复杂过滤的性能，尤其在 1.5.0 这种分布式后端下更为关键（参考）。

建议优先给所有高频过滤属性（如 country_code、status、multi_scene_mark 等）补充二级索引，索引生效后，后续多层遍历和过滤才会走索引，性能才能恢复到合理水平。否则即使主键起点，后续链式遍历和属性过滤依然会拖慢整体查询。

如需具体索引创建语句或进一步分析执行计划的瓶颈环节，可以继续补充说明。

To reply, just mention @dosu.

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