- html - 出于某种原因,IE8 对我的 Sass 文件中继承的 html5 CSS 不友好?
- JMeter 在响应断言中使用 span 标签的问题
- html - 在 :hover and :active? 上具有不同效果的 CSS 动画
- html - 相对于居中的 html 内容固定的 CSS 重复背景?
我很想得到您的帮助,了解为什么这个大量连接的查询需要大约 10 分钟才能在一个由 7 个表(总共 < 120K 行)组成的小型数据库上运行,并且最好能获得您关于如何在我们的小型数据库上加快查询速度的建议。四个节点的集群。我已将支持信息放在这里:https://gist.github.com/anonymous/8862796 (表列表、按表列出的字段列表以及表大小),但以下是查询和 EXPLAIN VERBOSE 输出。我对此查询运行 ANALYZE_WORKLOAD() ,然后按照其建议在所有表上运行 ANALYZE_STATISTICS 。这导致没有任何改善。然后,我执行了运行数据库设计器的第二个建议,这导致性能更慢。我非常感谢您的帮助。
个人资料信息
感谢以下有关“个人资料”的提示。我运行它并将结果放在这里:https://gist.github.com/anonymous/8935190 。它有 8K 行长,所以也许我没有正确运行它(要点中的详细信息)。问题:我如何开始分析它?
查询背景故事
查询之所以困惑,主要是因为它是为我们的机器学习研究软件的每次运行动态生成的,该软件必须应用各种条件,以图形方式遍历所涉及的 E-R 表。在本例中,路径为 [rates, movie, rates, ml_user, rates, movie, rates]。查询是在程序探索解决方案空间的过程中逐步建立的,这就是为什么(目前)没有@wumpz 和@Bohemian 下面善意而正确地建议的优化,例如消除子选择。这意味着我在短期内有点坚持目前的形式:-/
------------------------------
QUERY PLAN DESCRIPTION:
------------------------------
Opt Vertica Options
--------------------
PLAN_OUTPUT_SUPER_VERBOSE
EXPLAIN VERBOSE
SELECT relVarTable0.id AS id, relVarTable1.val, relVarTable2.val
FROM (SELECT id FROM rates) relVarTable0
LEFT JOIN
(SELECT rates1.id AS id, AVG(rates4.rating) AS val
FROM rates rates1, movie movie1, rates rates2, ml_user ml_user1, rates rates3, movie movie2, rates rates4
WHERE movie1.id = rates1.movie_id AND movie1.id = rates2.movie_id AND ml_user1.id = rates2.ml_user_id AND ml_user1.id = rates3.ml_user_id AND movie2.id = rates3.movie_id AND movie2.id = rates4.movie_id AND movie1.id <> movie2.id AND rates1.id <> rates2.id AND rates2.id <> rates3.id AND rates3.id <> rates4.id AND rates4.rating IS NOT NULL
GROUP BY rates1.id) relVarTable1
ON relVarTable0.id = relVarTable1.id
LEFT JOIN
(SELECT rates1.id AS id, rates1.rating AS val
FROM rates rates1
WHERE rates1.rating IS NOT NULL ) relVarTable2
ON relVarTable0.id = relVarTable2.id;
Access Path:
Sort Key: (V(1,1))
LDISTRIB_UNSEGMENTED
+-JOIN MERGEJOIN(inputs presorted) [LeftOuter] [Cost: 4489.000000, Rows: 75575.000000 Disk(B): 0.000000 CPU(B): 5441368.000000 Memory(B): 1209184.000000 Netwrk(B): 1209184.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 40] (PATH ID: 1) Inner (RESEGMENT)
| Join Cond: (relVarTable0.id = relVarTable2.id)
| Execute on: All Nodes
| Sort Key: (V(1,1))
| LDISTRIB_UNSEGMENTED
| +-- Outer -> JOIN MERGEJOIN(inputs presorted) [LeftOuter] [Cost: 4197.000000, Rows: 75575.000000 Disk(B): 0.000000 CPU(B): 1369200.000000 Memory(B): 0.000000 Netwrk(B): 604600.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 24] (PATH ID: 2) Outer (RESEGMENT)
| | Join Cond: (relVarTable0.id = relVarTable1.id)
| | Execute on: All Nodes
| | Sort Key: (V(1,1))
| | LDISTRIB_UNSEGMENTED
| | +-- Outer -> SELECT [Cost: 20.000000, Rows: 75575.000000 Disk(B): 0.000000 CPU(B): 0.000000 Memory(B): 0.000000 Netwrk(B): 0.000000 Parallelism: 1.000000 (NO STATISTICS)] [OutRowSz (B): 8] (PATH ID: 3)
| | | Execute on: All Nodes
| | | Sort Key: (rates.id, rates.ml_user_id, rates.movie_id, rates.rating)
| | | LDISTRIB_UNSEGMENTED
| | | +---> STORAGE ACCESS for rates [Cost: 20.000000, Rows: 75575.000000 Disk(B): 0.000000 CPU(B): 0.000000 Memory(B): 0.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 8] (PATH ID: 4)
| | | | Column Cost Aspects: [ Disk(B): 196608.000000 CPU(B): 0.000000 Memory(B): 604600.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 ]
| | | | Projection: movielens_test.rates_b0
| | | | Materialize: rates.id
| | | | Execute on: All Nodes
| | | | Sort Key: (rates.id, rates.ml_user_id, rates.movie_id, rates.rating)
| | | | LDISTRIB_SEGMENTED
| | +-- Inner -> SELECT [Cost: 4067.000000, Rows: 10000.000000 Disk(B): 0.000000 CPU(B): 0.000000 Memory(B): 0.000000 Netwrk(B): 0.000000 Parallelism: 1.000000 (NO STATISTICS)] [OutRowSz (B): 16] (PATH ID: 5)
| | | Execute on: All Nodes
| | | Sort Key: (rates.id)
| | | LDISTRIB_UNSEGMENTED
| | | +---> GROUPBY HASH (SORT OUTPUT) (GLOBAL RESEGMENT GROUPS) (LOCAL RESEGMENT GROUPS) [Cost: 4067.000000, Rows: 10000.000000 Disk(B): 0.000000 CPU(B): 6650600.000000 Memory(B): 640000.000000 Netwrk(B): 6890600.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 24] (PATH ID: 6)
| | | | Aggregates: sum_float(<SVAR>), count(<SVAR>)
| | | | Group By: rates1.id
| | | | Execute on: All Nodes
| | | | Sort Key: (rates.id)
| | | | LDISTRIB_SEGMENTED
| | | | +---> JOIN HASH [Cost: 2869.000000, Rows: 75575.000000 Disk(B): 0.000000 CPU(B): 12091944.000000 Memory(B): 3022960.000000 Netwrk(B): 1813776.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 88] (PATH ID: 7) Inner (RESEGMENT)
| | | | | Join Cond: (movie2.id = rates4.movie_id)
| | | | | Join Filter: (rates3.id <> rates4.id)
| | | | | Execute on: All Nodes
| | | | | Sort Key: (rates.id, rates.ml_user_id, rates.movie_id, rates.rating)
| | | | | LDISTRIB_UNSEGMENTED
| | | | | +-- Outer -> JOIN HASH [Cost: 2395.000000, Rows: 75575.000000 Disk(B): 0.000000 CPU(B): 9110592.000000 Memory(B): 41592.000000 Netwrk(B): 4246064.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 64] (PATH ID: 8) Outer (RESEGMENT)(LOCAL ROUND ROBIN) Inner (RESEGMENT)
| | | | | | Join Cond: (movie2.id = rates3.movie_id)
| | | | | | Join Filter: (movie1.id <> movie2.id)
| | | | | | Execute on: All Nodes
| | | | | | Runtime Filter: (SIP1(HashJoin): movie2.id)
| | | | | | Sort Key: (rates.id, rates.ml_user_id, rates.movie_id, rates.rating)
| | | | | | LDISTRIB_SEGMENTED
| | | | | | +-- Outer -> JOIN HASH [Cost: 1625.000000, Rows: 75575.000000 Disk(B): 0.000000 CPU(B): 10278200.000000 Memory(B): 3023000.000000 Netwrk(B): 1813800.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 56] (PATH ID: 9) Inner (RESEGMENT)
| | | | | | | Join Cond: (ml_user1.id = rates3.ml_user_id)
| | | | | | | Join Filter: (rates2.id <> rates3.id)
| | | | | | | Execute on: All Nodes
| | | | | | | Sort Key: (rates.id, rates.ml_user_id, rates.movie_id, rates.rating)
| | | | | | | LDISTRIB_UNSEGMENTED
| | | | | | | +-- Outer -> JOIN HASH [Cost: 1163.000000, Rows: 75575.000000 Disk(B): 0.000000 CPU(B): 5582544.000000 Memory(B): 141144.000000 Netwrk(B): 2465448.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 40] (PATH ID: 10) Outer (RESEGMENT)(LOCAL ROUND ROBIN) Inner (RESEGMENT)
| | | | | | | | Join Cond: (ml_user1.id = rates2.ml_user_id)
| | | | | | | | Execute on: All Nodes
| | | | | | | | Runtime Filter: (SIP2(HashJoin): ml_user1.id)
| | | | | | | | Sort Key: (rates.id, rates.ml_user_id, rates.movie_id, rates.rating)
| | | | | | | | LDISTRIB_SEGMENTED
| | | | | | | | +-- Outer -> JOIN HASH [Cost: 711.000000, Rows: 75575.000000 Disk(B): 0.000000 CPU(B): 8464400.000000 Memory(B): 2418400.000000 Netwrk(B): 1813800.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 32] (PATH ID: 11) Outer (RESEGMENT)(LOCAL ROUND ROBIN)
| | | | | | | | | Join Cond: (movie1.id = rates2.movie_id)
| | | | | | | | | Join Filter: (rates1.id <> rates2.id)
| | | | | | | | | Execute on: All Nodes
| | | | | | | | | Sort Key: (rates.id, rates.ml_user_id, rates.movie_id, rates.rating)
| | | | | | | | | LDISTRIB_SEGMENTED
| | | | | | | | | +-- Outer -> STORAGE ACCESS for rates2 [Cost: 59.000000, Rows: 75575.000000 Disk(B): 0.000000 CPU(B): 0.000000 Memory(B): 0.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 24] (PATH ID: 12)
| | | | | | | | | | Column Cost Aspects: [ Disk(B): 589824.000000 CPU(B): 0.000000 Memory(B): 1813800.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 ]
| | | | | | | | | | Projection: movielens_test.rates_b0
| | | | | | | | | | Materialize: rates2.id, rates2.ml_user_id, rates2.movie_id
| | | | | | | | | | Execute on: All Nodes
| | | | | | | | | | Sort Key: (rates.id, rates.ml_user_id, rates.movie_id, rates.rating)
| | | | | | | | | | LDISTRIB_SEGMENTED
| | | | | | | | | +-- Inner -> JOIN HASH [Cost: 268.000000, Rows: 75575.000000 Disk(B): 0.000000 CPU(B): 3064592.000000 Memory(B): 41592.000000 Netwrk(B): 1223064.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 16] (PATH ID: 13) Outer (RESEGMENT)(LOCAL ROUND ROBIN) Inner (RESEGMENT)
| | | | | | | | | | Join Cond: (movie1.id = rates1.movie_id)
| | | | | | | | | | Execute on: All Nodes
| | | | | | | | | | Sort Key: (rates.id, rates.ml_user_id, rates.movie_id, rates.rating)
| | | | | | | | | | LDISTRIB_SEGMENTED
| | | | | | | | | | +-- Outer -> STORAGE ACCESS for rates1 [Cost: 39.000000, Rows: 75575.000000 Disk(B): 0.000000 CPU(B): 0.000000 Memory(B): 0.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 16] (PATH ID: 14)
| | | | | | | | | | | Column Cost Aspects: [ Disk(B): 393216.000000 CPU(B): 0.000000 Memory(B): 1209200.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 ]
| | | | | | | | | | | Projection: movielens_test.rates_b0
| | | | | | | | | | | Materialize: rates1.id, rates1.movie_id
| | | | | | | | | | | Execute on: All Nodes
| | | | | | | | | | | Sort Key: (rates.id, rates.ml_user_id, rates.movie_id, rates.rating)
| | | | | | | | | | | LDISTRIB_SEGMENTED
| | | | | | | | | | +-- Inner -> STORAGE ACCESS for movie1 [Cost: 5.000000, Rows: 1733.000000 Disk(B): 0.000000 CPU(B): 0.000000 Memory(B): 0.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 8] (PATH ID: 15)
| | | | | | | | | | | Column Cost Aspects: [ Disk(B): 65536.000000 CPU(B): 0.000000 Memory(B): 13864.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 ]
| | | | | | | | | | | Projection: movielens_test.movie_b0
| | | | | | | | | | | Materialize: movie1.id
| | | | | | | | | | | Execute on: All Nodes
| | | | | | | | | | | Sort Key: (movie.id, movie.title, movie.year, movie.imdb_id, movie.rotten_tomatoes_id, movie.rotten_tomatoes_critic_score, movie.rotten_tomatoes_audience_score, movie.budget, movie.gross, movie.mpaa_rating, movie.runtime, movie.action, movie.adventure, movie.animation, movie.childrens, movie.comedy, movie.crime, movie.documentary, movie.drama, movie.fantasy, movie.film_noir, movie.horror, movie.musical, movie.mystery, movie.romance, movie.sci_fi, movie.thriller, movie.war, movie.western, movie.is_usa, movie.num_actors, movie.num_ratings)
| | | | | | | | | | | LDISTRIB_SEGMENTED
| | | | | | | | +-- Inner -> STORAGE ACCESS for ml_user1 [Cost: 5.000000, Rows: 5881.000000 Disk(B): 0.000000 CPU(B): 0.000000 Memory(B): 0.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 8] (PATH ID: 16)
| | | | | | | | | Column Cost Aspects: [ Disk(B): 65536.000000 CPU(B): 0.000000 Memory(B): 47048.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 ]
| | | | | | | | | Projection: movielens_test.ml_user_b0
| | | | | | | | | Materialize: ml_user1.id
| | | | | | | | | Execute on: All Nodes
| | | | | | | | | Sort Key: (ml_user.id, ml_user.gender, ml_user.age_range, ml_user.occupation, ml_user.zipcode, ml_user.num_ratings)
| | | | | | | | | LDISTRIB_SEGMENTED
| | | | | | | +-- Inner -> STORAGE ACCESS for rates3 [Cost: 59.000000, Rows: 75575.000000 Disk(B): 0.000000 CPU(B): 0.000000 Memory(B): 0.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 24] (PATH ID: 17)
| | | | | | | | Column Cost Aspects: [ Disk(B): 589824.000000 CPU(B): 0.000000 Memory(B): 1813800.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 ]
| | | | | | | | Projection: movielens_test.rates_b0
| | | | | | | | Materialize: rates3.id, rates3.ml_user_id, rates3.movie_id
| | | | | | | | Execute on: All Nodes
| | | | | | | | Sort Key: (rates.id, rates.ml_user_id, rates.movie_id, rates.rating)
| | | | | | | | LDISTRIB_SEGMENTED
| | | | | | +-- Inner -> STORAGE ACCESS for movie2 [Cost: 5.000000, Rows: 1733.000000 Disk(B): 0.000000 CPU(B): 0.000000 Memory(B): 0.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 8] (PATH ID: 18)
| | | | | | | Column Cost Aspects: [ Disk(B): 65536.000000 CPU(B): 0.000000 Memory(B): 13864.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 ]
| | | | | | | Projection: movielens_test.movie_b0
| | | | | | | Materialize: movie2.id
| | | | | | | Execute on: All Nodes
| | | | | | | Sort Key: (movie.id, movie.title, movie.year, movie.imdb_id, movie.rotten_tomatoes_id, movie.rotten_tomatoes_critic_score, movie.rotten_tomatoes_audience_score, movie.budget, movie.gross, movie.mpaa_rating, movie.runtime, movie.action, movie.adventure, movie.animation, movie.childrens, movie.comedy, movie.crime, movie.documentary, movie.drama, movie.fantasy, movie.film_noir, movie.horror, movie.musical, movie.mystery, movie.romance, movie.sci_fi, movie.thriller, movie.war, movie.western, movie.is_usa, movie.num_actors, movie.num_ratings)
| | | | | | | LDISTRIB_SEGMENTED
| | | | | +-- Inner -> STORAGE ACCESS for rates4 [Cost: 60.000000, Rows: 75574.000000 Disk(B): 0.000000 CPU(B): 0.000000 Memory(B): 0.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 24] (PUSHED GROUPING) Partial GroupBy: rates4.movie_id,rates4.id Partial Aggs: sum_float(<SVAR>),count(<SVAR>) (PATH ID: 19)
| | | | | | Column Cost Aspects: [ Disk(B): 589824.000000 CPU(B): 196608.000000 Memory(B): 1813784.000212 Netwrk(B): 0.000000 Parallelism: 4.000000 ]
| | | | | | Projection: movielens_test.rates_b0
| | | | | | Materialize: rates4.rating, rates4.id, rates4.movie_id
| | | | | | Filter: (rates4.rating IS NOT NULL)/* sel=0.999974 ndv= 500 */
| | | | | | Execute on: All Nodes
| | | | | | Sort Key: (rates.id, rates.ml_user_id, rates.movie_id, rates.rating)
| | | | | | LDISTRIB_SEGMENTED
| +-- Inner -> SELECT [Cost: 41.000000, Rows: 75574.000000 Disk(B): 0.000000 CPU(B): 0.000000 Memory(B): 0.000000 Netwrk(B): 0.000000 Parallelism: 1.000000 (NO STATISTICS)] [OutRowSz (B): 16] (PATH ID: 20)
| | Execute on: All Nodes
| | Sort Key: (rates.id, rates.ml_user_id, rates.movie_id, rates.rating)
| | LDISTRIB_UNSEGMENTED
| | +---> STORAGE ACCESS for rates1 [Cost: 41.000000, Rows: 75574.000000 Disk(B): 0.000000 CPU(B): 0.000000 Memory(B): 0.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 16] (PATH ID: 21)
| | | Column Cost Aspects: [ Disk(B): 393216.000000 CPU(B): 196608.000000 Memory(B): 1209184.000212 Netwrk(B): 0.000000 Parallelism: 4.000000 ]
| | | Projection: movielens_test.rates_b0
| | | Materialize: rates1.rating, rates1.id
| | | Filter: (rates1.rating IS NOT NULL)/* sel=0.999974 ndv= 500 */
| | | Execute on: All Nodes
| | | Sort Key: (rates.id, rates.ml_user_id, rates.movie_id, rates.rating)
| | | LDISTRIB_SEGMENTED
------------------------------
最佳答案
首先,我在您的解释计划中看到太多NO STATISTICS
。这是一个坏主意,您应该修复它。
看到连接中表的顺序了吗?创建了哈希联接,并且您正在对最大的表进行完整的表扫描。通过执行散列连接(小表连接大表)而不是散列连接(大表连接小表)来修复此问题。
movielens_test.rates
是否可以分区
最后一点,我总是这样做:
打开数据库日志并在运行查询时观察它。如果您的数据溢出到磁盘上,这可能是您的问题,因为您的排序数据大于分配的内存。
另一个选项是您在第一个子查询上创建预连接投影。但前提是您的数据不会遭受太多数据更改,因为预连接的投影在加载数据时非常糟糕。
关于sql - 如何加速小型 Vertica 数据库中缓慢的多连接查询(总行数约 120K,10 分钟),我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/21684140/
我们希望在 Vertica 数据库中设置一个可以查看某些系统表(投影、projection_storage 和 View )的用户,但我们不希望该用户成为 dbadmin,因为我们不希望他们拥有对这些
Vertica 允许将重复项插入到表中。我可以使用 'analyze_constraints' 函数查看那些。 如何从 Vertica 表中删除重复的行? 最佳答案 您应该尽量避免/限制对大量记录使用
我在 Vertica 上创建了一个表,我想在该表上创建一个索引。不过,我看不到如何在 Vertica 上创建索引。是否可以?如果是这样,我该怎么做? 最佳答案 Vertica 的速度取决于使用柱状投影
我在 Vertica 中有一张 table ,我无法删除它,因为我不是所有者。我如何查看表的所有者是谁? 最佳答案 如果您不是表的所有者,或者没有查看表的权限,那么您很可能无法查看所有者是谁: SEL
我在 Mysql 中有这个查询: UPDATE table1 AS a JOIN table2 AS b ON a.code=b.code AND b.rating < 3 SET a.Status
这是一个老问题 - 在 Vertica 中寻找最佳解决方案。想象一个有列的表格:- A, B, C, D, E 列 A-D 是 int 或 varchar,列 E 是 timestamptz 列,其默
我正在尝试通过 COPY DIRECT 从管道分隔的文本文件加载 HP Vertica 中的分段表。 COPY CSI.MKT_RSRCH_AGG_ALL FROM '/opt/vertica/CSI
Vertica 数据库可以用于 OLTP 数据吗? 如果是这样,这样做的利弊是什么? 寻找 Vertica 与 Oracle 的较量 :) 由于 Oracle 许可证如此昂贵,Vertica 会以更好
我在 Ubuntu 虚拟机中安装了 Vertica,我希望在启动过程中启动一个特定的数据库,而不是我必须登录、打开 admintools 并从那里开始。 那么,是否有一个命令行可以让我在没有用户交互的
我正在使用 python 与 vertica 进行通信。有没有一种优雅的方法来使用 pandas 数据框创建新的 vertica 表。我正在使用vertica-python 0.6.14。我知道的唯一
所以我有一个包含三个节点的 Hadoop 集群。 Vertica 位于集群上。 HDFS 上有 Parquet 文件(由 Hive 分区)。我的目标是使用 Vertica 查询这些文件。 现在我所做的
问题 1(共 2 个问题) 我正在尝试使用 Python 和 Uber 的 vertica-python 包将数据从 CSV 文件导入到 Vertica。问题在于纯空白数据元素被作为 NULL 加载到
我是 HP Vertica 的新手。我阅读了 HP Vertica 的安装文档。该文档完全基于 *ix 环境。所以,我的问题是我们也可以在 Windows 上安装 HP Vertica 吗? 另一件事
嗨,我已经在Ubuntu 10.10 32位版本的计算机中为vertica配置了DSN设置。 设置都很好,我已经对它们进行了交叉检查。 这是我的odbc.ini文件: [VerticaDSN]
我有以下 SQL Server 查询,需要将其转换为 Vertica 查询。现在的问题是 vertica 不支持多级相关子查询,因此在我的示例中,t3.a = t1.a 不起作用 select * f
SELECT ID,NAME,VALUE1,VALUE2 FROM my_table where ID=1 ; 查询会给我这样的输出 ID|NAME|VALUE1|VALUE2 1|XYZ|123|3
我在 Vertica 中遇到了与填充不存在的日期相关的问题。我在网上看到有人建议创建日历表的解决方案。 这是一个这样的 MYSQL来自stackoverflow的问题。 有没有办法使用另一个表中的 m
我正在调整一个网络分析工具来使用 Vertica作为数据库。我遇到了真正的问题 optimizing joins .我尝试为我的一些查询创建预连接预测,虽然它确实使查询速度非常快,但它减慢了数据加载到
我正在寻找一种使Vertica中的HBASE数据可用/可查询的方法。我已经看到Vertica与Hive的Metastore-HCatalog Connector具有良好的集成。 连接器可以从Hive
在vertica中有一个表:像这样测试: ID | name 1 | AA 2 | AB 2 | AC 3 | AD 3
我是一名优秀的程序员,十分优秀!