JAVA实现B+树索引:详细注释解析

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资源摘要信息:"B+树的实现与Java编程" 知识点: 1. B+树的基本概念: B+树是一种自平衡的树数据结构,它维护了数据的排序并允许搜索、顺序访问、插入和删除在对数时间内完成。B+树是B树的变种,它在数据库和文件系统的索引结构中得到了广泛应用。B+树的特点是所有数据记录都出现在叶子节点上,并且所有的叶子节点之间是通过指针连接的,这样的结构特别适合范围查询。 2. B+树与B树的区别: B树和B+树都是平衡多路搜索树,但它们在数据存储和查询上有细微差别: - 在B树中,非叶子节点既保存关键字也保存实际数据,因此非叶子节点也可以被命中;而在B+树中,非叶子节点只存储关键字和子节点的指针,所有数据记录都保存在叶子节点。 - B+树的叶子节点之间通过指针连接,因此对于范围查询和顺序访问非常高效。 - 由于B+树的非叶子节点不存储数据,因此在相同磁盘页大小的情况下,B+树可以拥有更多的子节点,减少树的高度,提高查询效率。 3. B+树的Java实现: Java作为一种高级编程语言,拥有丰富的库支持和良好的跨平台特性,非常适合用来实现数据结构。利用Java实现B+树需要编写能够处理关键字插入、删除和搜索操作的代码,并确保树结构的平衡性。Java中的对象和类机制能够方便地表示树的节点和结构。 4. 详细注释说明的重要性: 在编程实践中,编写带有详细注释的代码是十分重要的。注释可以帮助其他开发者(或者未来的自己)更快地理解代码的意图和实现方式,尤其是在实现复杂数据结构如B+树时。注释应包括数据结构的设计理念、关键算法的解释、代码段的功能描述以及特别注意的点等。 5. B+树在数据库索引中的应用: 数据库索引是提高数据库查询速度的重要手段。B+树由于其优秀的搜索效率和对范围查询的友好性,在数据库索引中占据重要地位。数据库系统通常采用B+树作为主要的索引数据结构,因为它能够在磁盘上高效地存储大量数据,并且能够保持查询性能稳定。 6. 文件压缩包“B+”: 文件压缩包“B+”可能包含了B+树实现的Java代码文件,这些文件可能包括类定义、数据结构定义以及主要的算法实现。文件名虽然只有“B+”,但是它暗示了压缩包内文件与B+树实现密切相关的事实。 7. 关键词标签的应用: 关键词标签如“b-树索引”,“b树”,“b_tree”,“java_b-tree”等,有助于在信息检索和分类中快速定位与B树相关的资源,便于学术研究者、开发者或用户寻找相关的技术资源和资料。 综合以上知识点,可以看出,B+树是一种高效的数据结构,尤其适用于数据库索引。Java作为一种编程语言,非常适合用来实现B+树的算法逻辑,并且详细的注释对于理解和维护代码至关重要。通过理解B+树的原理和实现,开发者可以有效地解决实际中的大数据搜索问题。

SQL优化以下语句(select f.file_name,a.content_id,c.fd_objectid level_id,c.level level_val,e.fd_objectid manage_id, ifnull((select count(fd_objectid) from message_receiver where MESSAGE_ID = e.fd_objectid), 0) SEND_PEOPLE_NUM, ifnull((select sum(case when reply_content is not null and reply_content != '' then 1 else 0 end) from message_receiver where MESSAGE_ID = e.fd_objectid), 0) reply_num, ifnull((select count(fd_objectid) from (select * from (select *,row_number() over(partition by seq,sendee_tel order by call_stat desc) flag from GROUPCALL_DETAILS) where flag = '1') where busi_id like concat('%', a.content_id) and busi_id like concat(a.event_id, '%')), 0) call_all, ifnull((select sum(case when call_stat like '%0%' then 1 else 0 end) from (select * from (select *,row_number() over(partition by seq,sendee_tel order by call_stat desc) flag from GROUPCALL_DETAILS) where flag = '1') where busi_id like concat('%', a.content_id) and busi_id like concat(a.event_id, '%')), 0) call_jt from NWYJ_SERVICE.ECM_EMYA_ORDER a left join MAP_EMEC_PLAN_CONTENT b on b.FD_OBJECTID = a.CONTENT_ID left join MAP_EMEC_PLAN c on c.FD_OBJECTID = b.RELATION_ID left join MAP_EMEC_ORG_RELATION d on d.FD_OBJECTID = b.ORG_RELATION_ID left join MESSAGE_MANAGE e on e.BUSI_ID = a.FD_OBJECTID left join MAP_EMEC_PLAN_ORG_TREE f on f.fd_objectid = d.org_id where a.event_id = #{eventId} and a.is_del = '0' and b.is_del = '0' and c.is_del = '0' and d.is_del = '0' and f.is_del = '0' and c.fd_objectid = #{levelId} and e.fd_objectid is not null)

2023-07-14 上传

select a.*, b.name activityName, c.name productName, c.member_max memberMax, d.status as complete, group_concat(if(e.face_value is null, "", e.face_value) separator "\n") as rewardAmount, group_concat(if(e.coupon_code is null, "", e.coupon_code ) separator "\n") as couponCode , group_concat(if(e.coupon_id is null, "", e.coupon_id) separator "\n") as caId from marketing_group_tool_group_member a left join marketing_group_tool_group_info d on a.group_id = d.id left join marketing_group_tool_activity b on a.activity_id = b.activity_id left join marketing_group_tool_product_base c on a.product_id = c.product_id left join marketing_group_tool_send_coupon_record e on (a.group_id = e.group_id and a.card_no = e.card_no) left join wx_recommend_organization organization on b.organization_id = organization.id where ((organization.tree_id between 2002000000000000 and 2002999999999999) or (organization.tree_id between 1000000000000000 and 1999999999999999) or (organization.tree_id between 2000000000000000 and 2999999999999999) or (organization.tree_id between 3000000000000000 and 3999999999999999) or (organization.tree_id between 4000000000000000 and 4999999999999999) or (organization.tree_id between 6000000000000000 and 6999999999999999) or (organization.tree_id between 5000000000000000 and 5999999999999999) or (organization.tree_id between 10000000000000000 and 10999999999999999) or (organization.tree_id between 8000000000000000 and 8999999999999999) or (organization.tree_id between 9000000000000000 and 9999999999999999) or (organization.tree_id between 11000000000000000 and 11999999999999999) or (organization.tree_id between 12000000000000000 and 12999999999999999) or (organization.tree_id between 13000000000000000 and 13999999999999999) or (organization.tree_id between 14000000000000000 and 14999999999999999) or (organization.tree_id between 15000000000000000 and 15999999999999999) or (organization.tree_id between 16000000000000000 and 16999999999999999) or (organization.tree_id between 17000000000000000 and 17999999999999999) ) and 1 = 1 group by a.id, a.activity_id , a.group_id , a.product_id , a.activity_referral_code, a.product_referral_code , a.openid , a.unionid , a.nickname , a.head_img , a.mobile , a.is_sub_buy , a.is_sub_progress , a.is_sub_success , a.sort , a.card_no , a.create_time , a.is_received_notice , a.is_received_progress , a.is_received_success , b.name, c.name, c.member_max , d.status order by a.id desc limit 0,20;

2023-06-13 上传

import numpy as np class Node: j = None theta = None p = None left = None right = None class DecisionTreeBase: def __init__(self, max_depth, feature_sample_rate, get_score): self.max_depth = max_depth self.feature_sample_rate = feature_sample_rate self.get_score = get_score def split_data(self, j, theta, X, idx): idx1, idx2 = list(), list() for i in idx: value = X[i][j] if value <= theta: idx1.append(i) else: idx2.append(i) return idx1, idx2 def get_random_features(self, n): shuffled = np.random.permutation(n) size = int(self.feature_sample_rate * n) selected = shuffled[:size] return selected def find_best_split(self, X, y, idx): m, n = X.shape best_score = float("inf") best_j = -1 best_theta = float("inf") best_idx1, best_idx2 = list(), list() selected_j = self.get_random_features(n) for j in selected_j: thetas = set([x[j] for x in X]) for theta in thetas: idx1, idx2 = self.split_data(j, theta, X, idx) if min(len(idx1), len(idx2)) == 0 : continue score1, score2 = self.get_score(y, idx1), self.get_score(y, idx2) w = 1.0 * len(idx1) / len(idx) score = w * score1 + (1-w) * score2 if score < best_score: best_score = score best_j = j best_theta = theta best_idx1 = idx1 best_idx2 = idx2 return best_j, best_theta, best_idx1, best_idx2, best_score def generate_tree(self, X, y, idx, d): r = Node() r.p = np.average(y[idx], axis=0) if d == 0 or len(idx)<2: return r current_score = self.get_score(y, idx) j, theta, idx1, idx2, score = self.find_best_split(X, y, idx) if score >= current_score: return r r.j = j r.theta = theta r.left = self.generate_tree(X, y, idx1, d-1) r.right = self.generate_tree(X, y, idx2, d-1) return r def fit(self, X, y): self.root = self.generate_tree(X, y, range(len(X)), self.max_depth) def get_prediction(self, r, x): if r.left == None and r.right == None: return r.p value = x[r.j] if value <= r.theta: return self.get_prediction(r.left, x) else: return self.get_prediction(r.right, x) def predict(self, X): y = list() for i in range(len(X)): y.append(self.get_prediction(self.root, X[i])) return np.array(y)

2023-06-08 上传