表空间操作 ================= http://${VEARCH_URL}代表master服务, $db_name是创建的库名, $space_name是创建的表空间名 创建表空间 ----------- 建表参数 >>>>>>>>>>>>>>>>>>>>>> :: curl -XPOST -H "content-type: application/json" -d' { "name": "space1", "partition_num": 1, "replica_num": 3, "fields": [ { "name": "field_string", "type": "string" }, { "name": "field_int", "type": "integer" }, { "name": "field_float", "type": "float", "index": { "name": "field_float", "type": "SCALAR" } }, { "name": "field_string_array", "type": "stringArray", "index": { "name": "field_string_array", "type": "SCALAR" } }, { "name": "field_int_index", "type": "integer", "index": { "name": "field_int_index", "type": "SCALAR" } }, { "name": "field_vector", "type": "vector", "dimension": 128, "index": { "name": "gamma", "type": "IVFPQ", "params": { "metric_type": "InnerProduct", "ncentroids": 4096, "nlinks": 32, "efConstruction": 160, "training_threshold": 819200 } } } ] } ' http://${VEARCH_URL}/dbs/$db_name/spaces 参数说明: +---------------+----------+--------+----------+------------------------------------------------+ | 字段标识 | 字段含义 | 类型 | 是否必填 | 备注 | +===============+==========+========+==========+================================================+ | name | 空间名称 | string | 是 | | +---------------+----------+--------+----------+------------------------------------------------+ | partition_num | 分片数量 | int | 是 | 单节点数据承载有效, 多个分片提升集群数据承载量 | +---------------+----------+--------+----------+------------------------------------------------+ | replica_num | 副本数量 | int | 是 | 通常设置为3来实现高可用 | +---------------+----------+--------+----------+------------------------------------------------+ | fields | 空间配置 | json | 是 | 定义表字段及类型,是否创建索引 | +---------------+----------+--------+----------+------------------------------------------------+ 1、name 不能为空, 不能以数字或下划线开头, 不要包含-（中划线），尽量不使用特殊字符等。 2、partition_num 指定表空间数据分片数量, 不同的分片可分布在不同的机器, 来避免单台机器的资源限制。 3、replica_num 副本数量, 建议设置成3, 表示每个分片数据有两个备份, 保证数据高可用。 fields字段说明 ::::::::::::::::::::: 1、表空间结构定义字段支持的类型(即type的值)有8种: string(keyword), stringArray, integer, long, float, double, date, vector。 2、string类型或者stringArray字段支持index属性, index定义是否创建索引, 创建索引后支持term过滤。 3、integer, long, float, double, date类型的字段支持index属性, index设置创建索引后支持数值范围过滤查询(range)。 4、vector 类型字段为特征字段, 一个表空间中支持多个特征字段, vector类型的字段支持的属性如下: +-------------+---------------+---------------+----------+----------------------------------------------+ |字段标识 |字段含义 |类型 |是否必填 |备注 | +=============+===============+===============+==========+==============================================+ |dimension |特征维数 |int |是 | | +-------------+---------------+---------------+----------+----------------------------------------------+ |format |归一化处理 |string |否 |设置为normalization对添加的特征向量归一化处理 | +-------------+---------------+---------------+----------+----------------------------------------------+ 5、主键字段，建表时会自动创建_id主键字段，当前不支持设置主键字段。 index索引说明 ::::::::::::::::::::: +----------+--------------+--------+----------+------+ | 字段标识 | 字段含义 | 类型 | 是否必填 | 备注 | +==========+==============+========+==========+======+ | name | 索引名称 | string | 是 | | +----------+--------------+--------+----------+------+ | type | 索引类型 | string | 是 | | +----------+--------------+--------+----------+------+ | params | 索引参数配置 | json | 否 | | +----------+--------------+--------+----------+------+ 1、index type 索引类型，目前支持二大类： 标量索引: SCALAR 向量索引: IVFPQ, HNSW, GPU_IVFPQ, GPU_IVFFLAT, IVFFLAT, BINARYIVF, FLAT 详细可看链接 https://github.com/vearch/vearch/wiki/Vearch%E7%B4%A2%E5%BC%95%E4%BB%8B%E7%BB%8D%E5%92%8C%E5%8F%82%E6%95%B0%E9%80%89%E6%8B%A9 标量索引只需设置name和type即可。 标量索引提供对标量数据的过滤功能, 开启方式参考“fields配置”中的第2条和第3条, 检索方式参考“查询”中的“filter json结构说明” 不同的向量索引类型需要的参数配置及默认值如下: IVFPQ 向量索引 '''''''''''''''''''''''''''''''''''' IVF系列向量索引需要训练，因此数据量未到训练阈值之前不会开始训练和构建索引。 IVFPQ可以与HNSW和OPQ组合使用。 如果要使用HNSW, 建议将ncentroids设置为较大的值。而在组合使用OPQ时, 训练占用的内存为 2 * training_threshold * dimension * sizeof(float), 因此对于HNSW和OPQ的组合使用, 训练将占用更多的内存并花费较长时间, 故要特别注意training_threshold的设置, 防止使用的太多内存。 ncentroids 根据经验，通常设置为单个分片数据量 ^ 0.5 * 4。注意是单个分片的数据量，不是整个space的数据量。 training_threshold: 对于IVFPQ, 在建立索引之前需要训练, 因此需要将training_threshold设置为合适的值, training_threshold可以是 ncentroids * 39 到 ncentroids * 256 之间的值。 training_threshold通常建议是 ncentroids * 200。training_threshold 会明显影响索引构建的效果，明显影响 检索速度和召回。 nsubvector 通常设置为向量维度除以2或者4，表示向量的压缩程度 ncentroids 和 training_threshold的设置同样对IVF系列其他索引适用，如IVFFLAT、GPU_IVFPQ、GPU_IVFFLAT。 如何组合使用HNSW和OPQ由params控制。如果同时设置HNSW和OPQ, 则将使用OPQ + IVF + HNSW + PQ, 建议将OPQ的nsubvector设置为与PQ的nsubvector相同。如果只想使用IVF + HNSW + PQ, 则只需要设置HNSW。如果您只想使用IVFPQ, 则无需在params中设置HNSW或OPQ。 +--------------------+---------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | 字段标识 | 字段含义 | 类型 | 是否必填 | 备注 | +====================+===========================+========+==========+=====================================================================================================+ | metric_type | 计算方式 | string | 是 | L2或者InnerProduct | +--------------------+---------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | ncentroids | 聚类中心数量 | int | 是 | 默认2048 | +--------------------+---------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | nsubvector | PQ拆分子向量大小 | int | 否 | 默认为向量维度除以2 | +--------------------+---------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | bucket_init_size | 倒排链表(IVF)初始化的大小 | int | 否 | 默认1000 | +--------------------+---------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | bucket_max_size | 倒排链表(IVF)最大容量 | int | 否 | 默认1280000 | +--------------------+---------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | training_threshold | 训练的数据量 | int | 否 | 默认ncentroids * 39,是每个分片训练需要的数据量, 不是space表的数据量，强烈建议设置为ncentroids * 200 | +--------------------+---------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | nprobe | 检索时查找的聚类中心数量 | int | 否 | 默认80 | +--------------------+---------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ :: "type": "IVFPQ", "params": { "metric_type": "InnerProduct", "ncentroids": 4096, "nsubvector": 64 } 您可以这样组合设置hnsw，特别注意当ncentroids较大（如超过10000）才有设置的必要: :: "type": "IVFPQ", "params": { "metric_type": "InnerProduct", "ncentroids": 65536, "nsubvector": 64, "hnsw" : { "nlinks": 32, "efConstruction": 200, "efSearch": 64 } } HNSW 向量索引 '''''''''''''''''''''''''''''''''''' +----------------+------------------------------------------+--------+----------+-----------------------------+ | 字段标识 | 字段含义 | 类型 | 是否必填 | 备注 | +================+==========================================+========+==========+=============================+ | metric_type | 计算方式 | string | 是 | L2或者InnerProduct | +----------------+------------------------------------------+--------+----------+-----------------------------+ | nlinks | 节点邻居数量 | int | 是 | 默认32 | +----------------+------------------------------------------+--------+----------+-----------------------------+ | efConstruction | 构图时寻找节点邻居过程中在图中遍历的深度 | int | 是 | 默认40,建议设置为120或者160 | +----------------+------------------------------------------+--------+----------+-----------------------------+ | efSearch | 检索时寻找节点邻居过程中在图中遍历的深度 | int | 否 | 默认40 | +----------------+------------------------------------------+--------+----------+-----------------------------+ :: "type": "HNSW", "params": { "metric_type": "InnerProduct", "nlinks": 32, "efConstruction": 160 } 注意: 1、向量存储只支持MemoryOnly 注意: 2、HNSW如果使用InnerProduct，强烈建议对向量进行归一化处理 FLAT 向量索引 '''''''''''''''''''''''''''''''''''' +---------------+------------------+------------+------------+----------------------------------------+ |字段标识 |字段含义 |类型 |是否必填 |备注 | +===============+==================+============+============+========================================+ |metric_type |计算方式 |string |是 |L2或者InnerProduct | +---------------+------------------+------------+------------+----------------------------------------+ :: "type": "FLAT", "params": { "metric_type": "InnerProduct" } 注意: 1、向量存储方式只支持MemoryOnly IVFFLAT 向量索引 '''''''''''''''''''''''''''''''''''' +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | 字段标识 | 字段含义 | 类型 | 是否必填 | 备注 | +====================+==========================+========+==========+=====================================================================================================+ | metric_type | 计算方式 | string | 是 | L2或者InnerProduct | +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | ncentroids | 聚类中心数量 | int | 是 | 默认2048 | +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | training_threshold | 训练的数据量 | int | 否 | 默认ncentroids * 39,是每个分片训练需要的数据量, 不是space表的数据量，强烈建议设置为ncentroids * 200 | +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | nprobe | 检索时查找的聚类中心数量 | int | 否 | 默认80 | +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ :: "type": "IVFFLAT", "params": { "metric_type": "InnerProduct", "ncentroids": 2048 } 注意: 1、向量存储方式只支持RocksDB GPU_IVFPQ 向量索引 '''''''''''''''''''''''''''''''''''' +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | 字段标识 | 字段含义 | 类型 | 是否必填 | 备注 | +====================+==========================+========+==========+=====================================================================================================+ | metric_type | 计算方式 | string | 是 | L2或者InnerProduct | +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | ncentroids | 聚类中心数量 | int | 是 | 默认2048 | +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | nsubvector | PQ拆分子向量大小 | int | 否 | 默认64 | +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | training_threshold | 训练的数据量 | int | 否 | 默认ncentroids * 39,是每个分片训练需要的数据量, 不是space表的数据量，强烈建议设置为ncentroids * 200 | +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | nprobe | 检索时查找的聚类中心数量 | int | 否 | 默认80 | +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ :: "type": "GPU_IVFPQ", "params": { "metric_type": "InnerProduct", "ncentroids": 2048, "nsubvector": 64 } GPU_IVFFLAT 向量索引 '''''''''''''''''''''''''''''''''''' +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | 字段标识 | 字段含义 | 类型 | 是否必填 | 备注 | +====================+==========================+========+==========+=====================================================================================================+ | metric_type | 计算方式 | string | 是 | L2或者InnerProduct | +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | ncentroids | 聚类中心数量 | int | 是 | 默认2048 | +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | training_threshold | 训练的数据量 | int | 否 | 默认ncentroids * 39,是每个分片训练需要的数据量, 不是space表的数据量，强烈建议设置为ncentroids * 200 | +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ | nprobe | 检索时查找的聚类中心数量 | int | 否 | 默认80 | +--------------------+--------------------------+--------+----------+-----------------------------------------------------------------------------------------------------+ :: "type": "GPU_IVFFLAT", "params": { "metric_type": "InnerProduct", "ncentroids": 2048, "nsubvector": 64 } BINARYIVF 向量索引 '''''''''''''''''''''''''''''''''''' +--------------------+--------------------------+------+----------+-----------------------------------------------------------------------------------------------------+ | 字段标识 | 字段含义 | 类型 | 是否必填 | 备注 | +====================+==========================+======+==========+=====================================================================================================+ | ncentroids | 聚类中心数量 | int | 是 | 默认2048 | +--------------------+--------------------------+------+----------+-----------------------------------------------------------------------------------------------------+ | training_threshold | 训练的数据量 | int | 否 | 默认ncentroids * 39,是每个分片训练需要的数据量, 不是space表的数据量，强烈建议设置为ncentroids * 200 | +--------------------+--------------------------+------+----------+-----------------------------------------------------------------------------------------------------+ | nprobe | 检索时查找的聚类中心数量 | int | 否 | 默认80 | +--------------------+--------------------------+------+----------+-----------------------------------------------------------------------------------------------------+ :: "type": "BINARYIVF", "params": { "ncentroids": 2048 } 注意: 1、向量长度是8的倍数 自定义表空间分片规则 >>>>>>>>>>>>>>>>>>>>>> 创建表空间时指定自定义分片规则，当数据存在过期需求，可以通过此方式直接淘汰对应分片组的数据 :: curl -XPOST -H "content-type: application/json" -u "root:secret" -d' { "name": "space1", "partition_num": 1, "replica_num": 1, "fields": [ { "name": "field_string", "type": "string" }, { "name": "field_int", "type": "integer" }, { "name": "field_float", "type": "float", "index": { "name": "field_float", "type": "SCALAR" } }, { "name": "field_string_array", "type": "stringArray", "index": { "name": "field_string_array", "type": "SCALAR" } }, { "name": "field_int_index", "type": "integer", "index": { "name": "field_int_index", "type": "SCALAR" } }, { "name": "field_date", "type": "date", "index": { "name": "field_date", "type": "SCALAR" } }, { "name": "field_vector", "type": "vector", "dimension": 128, "index": { "name": "gamma", "type": "IVFPQ", "params": { "metric_type": "InnerProduct", "ncentroids": 2048, "nlinks": 32, "efConstruction": 40 } } } ], "partition_rule": { "type": "RANGE", "field": "field_date", "ranges": [ { "name": "p0", "value": "2024-07-15" }, { "name": "p1", "value": "2024-07-16" }, { "name": "p2", "value": "2024-07-17" } ] } } ' http://${VEARCH_URL}/dbs/$db_name/spaces partition rule详细格式: +----------+----------------+--------+----------+-------------------------------------------------------------------------------------------------------------------------+ | 字段标识 | 字段含义 | 类型 | 是否必须 | 备注 | +==========+================+========+==========+=========================================================================================================================+ | type | 自定义分片类型 | string | 是 | 目前只支持 RANGE | +----------+----------------+--------+----------+-------------------------------------------------------------------------------------------------------------------------+ | field | 自定义分片字段 | string | 是 | 需要是fields里面的字段，目前只支持date类型，和_id组成共同主键，即_id相同但是partition field对应的值不同是两条不同的数据 | +----------+----------------+--------+----------+-------------------------------------------------------------------------------------------------------------------------+ | ranges | 自定义分片范围 | json | 是 | 每个分片field值对应的范围,name为分片组的名称，value是对应的值的阈值且需要是递增的 | +----------+----------------+--------+----------+-------------------------------------------------------------------------------------------------------------------------+ date类型目前只支持到秒级，支持两种方式传入：日期格式的字符串和时间戳，为保证数据的准确性需传入秒级的时间戳，不然内部转换会导致数据准确度丢失。 每个range会对应一个分片组，分片组的分片数量为partition_num，即设置了partition rule的表空间总共会有 len(ranges) * partition_num * replica_num 个分片, 其中len(ranges) * partition_num 个分片为完整数据。 range 范围规则为less than，不包含边界。如 “2024-07-17”则会按照 “2024-07-17 00：00：00”进行比较，这个分片组对应的就是小于“2024-07-17 00：00：00”的值，不包含“2024-07-17 00：00：00”。 删除分片组 >>>>>>>>>>>> :: curl -XPUT -H "content-type: application/json" -d' { "partition_name": "p0", "operator_type": "DROP" } ' http://${VEARCH_URL}/dbs/$db_name/spaces/$space_name 添加分片组 >>>>>>>>>>>> :: curl -XPUT -H "content-type: application/json" -d' { "operator_type": "ADD", "partition_rule": { "type": "RANGE", "ranges": [ { "name": "p3", "value": "2024-07-18" }, { "name": "p4", "value": "2024-07-19" } ] } } ' http://${VEARCH_URL}/dbs/$db_name/spaces/$space_name 查看表空间 ------------ :: curl -XGET http://${VEARCH_URL}/dbs/$db_name/spaces/$space_name 返回数据详细格式: +----------+----------+--------+--------------+------+ | 字段标识 | 字段含义 | 类型 | 是否一定返回 | 备注 | +==========+==========+========+==============+======+ | code | 返回码 | int | 是 | | +----------+----------+--------+--------------+------+ | msg | 返回信息 | string | 否 | | +----------+----------+--------+--------------+------+ | data | 返回数据 | json | 否 | | +----------+----------+--------+--------------+------+ data字段详细信息: +---------------+----------------+------------+--------------+---------------------------------------+ | 字段标识 | 字段含义 | 类型 | 是否一定返回 | 备注 | +===============+================+============+==============+=======================================+ | space_name | 表名 | string | 是 | | +---------------+----------------+------------+--------------+---------------------------------------+ | db_name | 库名 | string | 是 | | +---------------+----------------+------------+--------------+---------------------------------------+ | doc_num | 表文档数量 | uint64 | 是 | | +---------------+----------------+------------+--------------+---------------------------------------+ | partition_num | 分片数量 | int | 是 | 对表所有数据进行分片 | +---------------+----------------+------------+--------------+---------------------------------------+ | replica_num | 副本数量 | int | 是 | 通常设置为3来实现高可用 | +---------------+----------------+------------+--------------+---------------------------------------+ | schema | 表结构 | json | 是 | | +---------------+----------------+------------+--------------+---------------------------------------+ | status | 表状态 | string | 是 | red表示表有异常,green正常, yellow预警 | +---------------+----------------+------------+--------------+---------------------------------------+ | partitions | 表分片详细信息 | json | 是 | 参数控制是否返回更多详细信息 | +---------------+----------------+------------+--------------+---------------------------------------+ | errors | 表异常信息 | string数组 | 否 | | +---------------+----------------+------------+--------------+---------------------------------------+ 返回值格式如下: :: { "code": 0, "data": { "space_name": "ts_space", "db_name": "ts_db", "doc_num": 0, "partition_num": 1, "replica_num": 3, "schema": { "fields": [ { "name": "field_string", "type": "string" }, { "name": "field_int", "type": "integer" }, { "name": "field_float", "type": "float", "index": { "name": "field_float", "type": "SCALAR" } }, { "name": "field_string_array", "type": "stringArray", "index": { "name": "field_string_array", "type": "SCALAR" } }, { "name": "field_int_index", "type": "integer", "index": { "name": "field_int_index", "type": "SCALAR" } }, { "name": "field_vector", "type": "vector", "dimension": 128, "index": { "name": "gamma", "type": "IVFPQ", "params": { "metric_type": "InnerProduct", "ncentroids": 2048, "nlinks": 32, "efConstruction": 40 } } } ] }, "status": "green", "partitions": [ { "pid": 1, "replica_num": 1, "status": 4, "color": "green", "ip": "x.x.x.x", "node_id": 1, "index_status": 0, "index_num": 0, "max_docid": -1 }, { "pid": 2, "replica_num": 1, "status": 4, "color": "green", "ip": "x.x.x.x", "node_id": 2, "index_status": 0, "index_num": 0, "max_docid": -1 }, { "pid": 3, "replica_num": 1, "status": 4, "color": "green", "ip": "x.x.x.x", "node_id": 3, "index_status": 0, "index_num": 0, "max_docid": -1 } ], } } 查看表partitions更多详细信息 :: curl -XGET http://${VEARCH_URL}/dbs/$db_name/spaces/$space_name?detail=true 返回值格式如下: :: { "code": 0, "data": { "space_name": "ts_space", "db_name": "ts_db", "doc_num": 0, "partition_num": 1, "replica_num": 3, "schema": { "fields": [ { "name": "field_string", "type": "string" }, { "name": "field_int", "type": "integer" }, { "name": "field_float", "type": "float", "index": { "name": "field_float", "type": "SCALAR" } }, { "name": "field_string_array", "type": "stringArray", "index": { "name": "field_string_array", "type": "SCALAR" } }, { "name": "field_int_index", "type": "integer", "index": { "name": "field_int_index", "type": "SCALAR" } }, { "name": "field_vector", "type": "vector", "dimension": 128, "index": { "name": "gamma", "type": "IVFPQ", "params": { "metric_type": "InnerProduct", "ncentroids": 2048, "nlinks": 32, "efConstruction": 40 } } } ] }, "status": "green", "partitions": [ { "pid": 1, "replica_num": 1, "path": "/export/Data/datas/", "status": 4, "color": "green", "ip": "x.x.x.x", "node_id": 1, "raft_status": { "ID": 1, "NodeID": 1, "Leader": 1, "Term": 1, "Index": 1, "Commit": 1, "Applied": 1, "Vote": 1, "PendQueue": 0, "RecvQueue": 0, "AppQueue": 0, "Stopped": false, "RestoringSnapshot": false, "State": "StateLeader", "Replicas": { "1": { "Match": 1, "Commit": 1, "Next": 2, "State": "ReplicaStateProbe", "Snapshoting": false, "Paused": false, "Active": true, "LastActive": "2024-03-18T09: 59: 17.095112556+08: 00", "Inflight": 0 } } }, "index_status": 0, "index_num": 0, "max_docid": -1 }, { "pid": 2, "replica_num": 1, "path": "/export/Data/datas/", "status": 4, "color": "green", "ip": "x.x.x.x", "node_id": 2, "raft_status": { "ID": 2, "NodeID": 1, "Leader": 1, "Term": 1, "Index": 1, "Commit": 1, "Applied": 1, "Vote": 1, "PendQueue": 0, "RecvQueue": 0, "AppQueue": 0, "Stopped": false, "RestoringSnapshot": false, "State": "StateLeader", "Replicas": { "1": { "Match": 1, "Commit": 1, "Next": 2, "State": "ReplicaStateProbe", "Snapshoting": false, "Paused": false, "Active": true, "LastActive": "2024-03-18T09: 59: 17.095112556+08: 00", "Inflight": 0 } } }, "index_status": 0, "index_num": 0, "max_docid": -1 }, { "pid": 3, "replica_num": 1, "path": "/export/Data/datas/", "status": 4, "color": "green", "ip": "x.x.x.x", "node_id": 3, "raft_status": { "ID": 3, "NodeID": 1, "Leader": 1, "Term": 1, "Index": 1, "Commit": 1, "Applied": 1, "Vote": 1, "PendQueue": 0, "RecvQueue": 0, "AppQueue": 0, "Stopped": false, "RestoringSnapshot": false, "State": "StateLeader", "Replicas": { "1": { "Match": 1, "Commit": 1, "Next": 2, 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