indexing

问题 After reading about query optimization techniques I came to know about the below techniques. 1. Indexing - bitmap and BTree 2. Partitioning 3. Bucketing I got the difference between partitioning and bucketing, and when to use them but I'm still confused how indexes actually work. Where is the metadata for index is stored? Is it the namenode which is storing it? I.e., actually while creating partitions or buckets we can see multiple directories in hdfs which explains the query performance

问题 After reading about query optimization techniques I came to know about the below techniques. 1. Indexing - bitmap and BTree 2. Partitioning 3. Bucketing I got the difference between partitioning and bucketing, and when to use them but I'm still confused how indexes actually work. Where is the metadata for index is stored? Is it the namenode which is storing it? I.e., actually while creating partitions or buckets we can see multiple directories in hdfs which explains the query performance

问题 After reading about query optimization techniques I came to know about the below techniques. 1. Indexing - bitmap and BTree 2. Partitioning 3. Bucketing I got the difference between partitioning and bucketing, and when to use them but I'm still confused how indexes actually work. Where is the metadata for index is stored? Is it the namenode which is storing it? I.e., actually while creating partitions or buckets we can see multiple directories in hdfs which explains the query performance

问题 I have a Pandas DataFrame with a named index. I want to pass it off to a piece off code that takes a DataFrame, a column name, and some other stuff, and does a bunch of work involving that column. Only in this case the column I want to highlight is the index, but giving the index's label to this piece of code doesn't work because you can't extract an index like you can a regular column. For example, I can construct a DataFrame like this: import pandas as pd, numpy as np df=pd.DataFrame({'name

问题 I have a Pandas DataFrame with a named index. I want to pass it off to a piece off code that takes a DataFrame, a column name, and some other stuff, and does a bunch of work involving that column. Only in this case the column I want to highlight is the index, but giving the index's label to this piece of code doesn't work because you can't extract an index like you can a regular column. For example, I can construct a DataFrame like this: import pandas as pd, numpy as np df=pd.DataFrame({'name

问题 Is there any way I can retain the original index of my large dataframe after I perform a groupby? The reason I need to this is because I need to do an inner merge back to my original df (after my groupby) to regain those lost columns. And the index value is the only 'unique' column to perform the merge back into. Does anyone know how I can achieve this? My DataFrame is quite large. My groupby looks like this: df.groupby(['col1', 'col2']).agg({'col3': 'count'}).reset_index() This drops my

问题 Is there any way I can retain the original index of my large dataframe after I perform a groupby? The reason I need to this is because I need to do an inner merge back to my original df (after my groupby) to regain those lost columns. And the index value is the only 'unique' column to perform the merge back into. Does anyone know how I can achieve this? My DataFrame is quite large. My groupby looks like this: df.groupby(['col1', 'col2']).agg({'col3': 'count'}).reset_index() This drops my

问题 I have a series of extremely similar queries that I run against a table of 1.4 billion records (with indexes), the only problem is that at least 10% of those queries take > 100x more time to execute than others. I ran an explain plan and noticed that the for the fast queries (roughly 90%) Oracle is using an index range scan; on the slow ones, it's using a full index scan. Is there a way to force Oracle to do an index range scan? 回答1: To "force" Oracle to use an index range scan, simply use an

问题 I have a series of extremely similar queries that I run against a table of 1.4 billion records (with indexes), the only problem is that at least 10% of those queries take > 100x more time to execute than others. I ran an explain plan and noticed that the for the fast queries (roughly 90%) Oracle is using an index range scan; on the slow ones, it's using a full index scan. Is there a way to force Oracle to do an index range scan? 回答1: To "force" Oracle to use an index range scan, simply use an

问题 Here are the compound index and single index I have for this Collection: ///db.Collection.getIndexes() /* 1 */ { "v" : 2, "key" : { "_id" : 1 }, "name" : "_id_", "ns" : "service.Collection" }, /* 2 */ { "v" : 2, "key" : { "FirstId" : 1, "SecondId" : 1, "CreationTime" : -1 }, "name" : "FirstIdSecondIdCreationTime", "collation" : { "locale" : "en", "caseLevel" : false, "caseFirst" : "off", "strength" : 1, "numericOrdering" : false, "alternate" : "non-ignorable", "maxVariable" : "punct",