Ruth  Nabimanya

Ruth Nabimanya

1635763860

Automated Index Management for Database Automation Series

In this tutorial, we'll learn Automated Index Management for Database Automation Series

Managing databases can be difficult, but it doesn't have to be. Most aspects of database management can be automated, and with a platform such as MongoDB Atlas, the tools are not only available, but they're easy to use. In this series, we'll chat with Rez Kahn, Lead Product Manager at MongoDB, to learn about some of the ways Atlas automates the various tasks associated with deploying, scaling, and ensuring efficient performance of your databases.
 

#database #mongodb 

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Automated Index Management for Database Automation Series
Origin Scale

Origin Scale

1620805745

Automation Management System

Want to try automated inventory management system for small businesses? Originscale automation software automate your data flow across orders, inventory, and purchasing. TRY FOR FREE

#automation #automation software #automated inventory management #automated inventory management system #automation management system #inventory automation

Edison  Stark

Edison Stark

1598535540

How Indexes Work in Nebula Graph - DZone Database

Why Indexes Are Needed in a Graph Database

Indexes are an indispensable function in a database system. Graph databases are no exception.

An index is actually a sorted data structure in the database management system. Different database systems adopt different sorting structures.

Popular index types include:

  • B-Tree index
  • B±Tree index
  • B*-Tree index
  • Hash index
  • Bitmap index
  • Inverted index

Each of them uses their own sorting algorithms.

A database index allows efficient data retrieval from databases. Despite of the query performance improvement, there are some disadvantages of indexes:

  • It takes time to create and maintain indexes, which scales with dataset size.
  • Indexes need extra physical storage space.
  • It takes more time to insert, delete, and update data because the index also needs to be maintained synchronously.

Taking the above into consideration, Nebula Graph now supports indexes for more efficient retrieves on properties.

This post gives a detailed introduction to the design and practice of indexes in Nebula Graph.

Core Concepts to Understand Indexes in Nebula Graph

Below is a list of common Nebula Graph index terms we use across the post.

  • Tag: A label associated with a list of properties. Each vertex can associate with multiple tags. Tag is identified with a TagID. You can regard tag as a node table in SQL.
  • Edge: Similar to tag, edge type is a cluster of properties on edges. You can regard edge type as an edge table in SQL.
  • Property: The name-value pairs on tag or edge. Its data type is determined by the tag or edge type.
  • Partition: The minimum logical storage unit of Nebula Graph. A StorageEngine can contain multiple partitions. Partition is divided into leader and follower. We use Raft to guarantee data consistency between leader and follower.
  • Graph space: A physically isolated space for a specific graph. Tags and edge types in one graph are independent with those in another graph. A Nebula Graph cluster can have multiple graph spaces.
  • Index: Index in this post refers specifically to the index of ~~ ~~tag or edge type properties. Its data type depends on tag or edge type.
  • TagIndex: An index created for a tag. You can create multiple indexes for the same tag. Cross-tag composite index is yet to be supported.
  • EdgeIndex: An index created for an edge type. Similarly, you can create multiple indexes for the same edge type. Cross-edge-type composite index is yet to be supported.
  • Scan Policy: The policy to scan indexes. Usually, there are multiple methods to scan indexes to execute one query statement, but the scan policy itself gets to decide which method to use ultimately.
  • Optimizer: Optimize query conditions, such as sorting, splitting, and merging sub-expression nodes of the expression tree of the where clause. It’s used to obtain higher query efficiency.

What’s Required for Indexes to Work in a Graph Database

There are two typical ways to query data in Nebula Graph, or more generally in a graph database:

  1. One is starting from a vertex, retrieving its (N-hop) neighbors along certain edge types.
  2. Another is retrieving vertices or edges which contain specified property values.

In the latter scenario, a high-performance scan is needed to fetch the edges or vertices as well as the property values.

In order to improve the query efficiency of property values, we’ve implemented indexes in Nebula Graph. By sorting the property values of edges or vertices, users can quickly locate a certain property and avoid full scan.

Here’s what we found are required for indexes to work in a graph database:

  • Supporting indexes for properties on tags and edge types.
  • Supporting analysis and generation of index scanning strategy.
  • Supporting index management such as create index, rebuild index, show index, etc.

How Indexes Are Stored in Nebula Graph

Below is a diagram of how indexes are stored in Nebula Graph. Indexes are a part of Nebula Graph’s Storage Service so we place them in the big picture of its storage architecture.

Seen from the above figure, each Storage Server can contain multiple Storage Engines, each Storage Engine can contain multiple Partitions.

Different Partitions are synchronized via Raft protocol. Each Partition contains both data and indexes. The data and indexes of the same vertex or edge will be stored in the same Partition.

#tutorial #graph database #index #database indexes #nebula graph #database

Ruth  Nabimanya

Ruth Nabimanya

1620640920

How to Efficiently Choose the Right Database for Your Applications

Finding the right database solution for your application is not easy. Learn how to efficiently find a database for your applications.

Finding the right database solution for your application is not easy. At iQIYI, one of the largest online video sites in the world, we’re experienced in database selection across several fields: Online Transactional Processing (OLTP), Online Analytical Processing (OLAP), Hybrid Transaction/Analytical Processing (HTAP), SQL, and NoSQL.

Today, I’ll share with you:

  • What criteria to use for selecting a database.
  • What databases we use at iQIYI.
  • Some decision models to help you efficiently pick a database.
  • Tips for choosing your database.

I hope this post can help you easily find the right database for your applications.

#database architecture #database application #database choice #database management system #database management tool

 iOS App Dev

iOS App Dev

1625133780

SingleStore: The One Stop Shop For Everything Data

  • SingleStore works toward helping businesses embrace digital innovation by operationalising “all data through one platform for all the moments that matter”

The pandemic has brought a period of transformation across businesses globally, pushing data and analytics to the forefront of decision making. Starting from enabling advanced data-driven operations to creating intelligent workflows, enterprise leaders have been looking to transform every part of their organisation.

SingleStore is one of the leading companies in the world, offering a unified database to facilitate fast analytics for organisations looking to embrace diverse data and accelerate their innovations. It provides an SQL platform to help companies aggregate, manage, and use the vast trove of data distributed across silos in multiple clouds and on-premise environments.

**Your expertise needed! **Fill up our quick Survey

#featured #data analytics #data warehouse augmentation #database #database management #fast analytics #memsql #modern database #modernising data platforms #one stop shop for data #singlestore #singlestore data analytics #singlestore database #singlestore one stop shop for data #singlestore unified database #sql #sql database

Ruth  Nabimanya

Ruth Nabimanya

1620633584

System Databases in SQL Server

Introduction

In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.

System Database

Fig. 1 System Databases

There are five system databases, these databases are created while installing SQL Server.

  • Master
  • Model
  • MSDB
  • Tempdb
  • Resource
Master
  • This database contains all the System level Information in SQL Server. The Information in form of Meta data.
  • Because of this master database, we are able to access the SQL Server (On premise SQL Server)
Model
  • This database is used as a template for new databases.
  • Whenever a new database is created, initially a copy of model database is what created as new database.
MSDB
  • This database is where a service called SQL Server Agent stores its data.
  • SQL server Agent is in charge of automation, which includes entities such as jobs, schedules, and alerts.
TempDB
  • The Tempdb is where SQL Server stores temporary data such as work tables, sort space, row versioning information and etc.
  • User can create their own version of temporary tables and those are stored in Tempdb.
  • But this database is destroyed and recreated every time when we restart the instance of SQL Server.
Resource
  • The resource database is a hidden, read only database that holds the definitions of all system objects.
  • When we query system object in a database, they appear to reside in the sys schema of the local database, but in actually their definitions reside in the resource db.

#sql server #master system database #model system database #msdb system database #sql server system databases #ssms #system database #system databases in sql server #tempdb system database