Awesome  Rust

Awesome Rust

1665296280

ReadySet: A Lightweight SQL Caching Engine Written in Rust

ReadySet 

ReadySet is a SQL caching engine designed to help developers enhance the performance and scalability of their existing database-backed applications.

What is ReadySet?

ReadySet is a lightweight SQL caching engine that precomputes frequently-accessed query results and automatically keeps these results up-to-date over time as the underlying data in your database changes. ReadySet is wire-compatible with MySQL and Postgres and can be adopted without code changes.


 


 

ReadySet acts as both a SQL cache and a proxy– when you first connect ReadySet to your application, it defaults to proxying all of your queries to your backing database, so it doesn't change the behavior of your application.

From there, you can monitor the performance of your queries using the ReadySet dashboard, and cache a subset of them in ReadySet. ReadySet automatically keeps cached results up-to-date over time as the underlying data in your database changes, so there's no need to write cache maintenance logic.

Getting Started

Try ReadySet in 5 mins

The ReadySet orchestrator is a command line tool that uses Docker Compose to spin up a ReadySet instance on your local machine. The following command downloads and runs the ReadySet orchestrator:

bash -c "$(curl -sSL https://launch.readyset.io)"

The orchestrator will prompt you to select a wire protocol, and to connect ReadySet to an existing database (or create a new one).

Connect to ReadySet

  • To connect to ReadySet via the MySQL or Postgres command line, run the command that the orchestrator prints after successfully deploying a ReadySet instance.
  • To connect ReadySet to your application, use the ReadySet connection string (printed by the orchestrator after successfully deploying ReadySet) in the same way you would if it were a MySQL or Postgres connection string. Check out our docs for more information.

Once you connect to ReadySet, you can start issuing queries as you would with a traditional database.

Monitor Query Performance

By default, all queries sent to ReadySet are proxied to the backing database. You can see the current list of proxied queries and their performance profiles via the ReadySet dashboard which is accessible on http://localhost:4000.

Cache Queries

ReadySet supports caching both prepared statements (i.e. parameterized queries) and one-off queries. To cache a query in ReadySet, you’ll need either the query ID (which can be found in the dashboard, or by running SHOW CACHES) or the full query text (i.e. the SELECT statement).

From there, you can run CREATE CACHE FROM <query ID> or CREATE CACHE FROM <select statement> via the MySQL or Postgres client.

Write Handling

You can either send writes to ReadySet or directly to your backing database. If you send ReadySet a write, it will be proxied to your backing database. ReadySet waits to receive updates from your database's bin logs before updating the cached state to reflect those writes.

Note: The ReadySet Orchestrator is updated frequently with releases built off our internal ReadySet repository. Because of this, the orchestrator may have features not yet in our public repo. We're working on making our live codebase available on Github.

FAQs

Q: How does ReadySet work under the hood?

A: The heart of ReadySet is a query engine based on partially-stateful, streaming data flow. To learn more about how it works, see our docs.

Q: How does ReadySet keep cached state up to date?

A: ReadySet receives updates about data changes from your backing database via binlog replication and uses those updates to automatically update its internal state.

Q: Do I have to send all of my database traffic to ReadySet?

A: You can if you want to, but it’s not required. You can manually route a subset of your traffic to ReadySet (as you would with a traditional database read replica), or you can send all of it to ReadySet. It's important to note that not all of the queries sent to ReadySet need to be cached– you have fine-grained control over what is cached vs. what is proxied.

Documentation

For more information, check out our full docs site here.

Join the Community

For questions or support, join us on the ReadySet Community Discord, post questions on our Github forum, or schedule an office hours chat with our team.

Everyone is welcome!

Development

Prerequisites

Install Dependencies

Prior to running ReadySet, you may need to install the following dependencies: clang libclang-dev libssl-dev liblz4-dev build-essential

macOS:

brew install lz4
brew install openssl@1.1

Ubuntu:

sudo apt update && sudo apt install -y build-essential libssl-dev pkg-config llvm clang liblz4-dev cmake

Install Rust

ReadySet is written entirely in Rust. If you don’t already have Rust installed, you can install it via rustup (select the version of ‘nightly’ specified in the rust-toolchain file):

curl https://sh.rustup.rs -sSf | sh

Runtime Dependencies: Upstream Database and Consul

Local Development

To streamline local development, all runtime dependencies (Consul, MySQL, Postgres) can be run with:

cp docker-compose.override.yml.example docker-compose.override.yml
docker-compose up -d

Full Deployment

ReadySet runs alongside a backing MySQL or Postgres database and uses Consul for leader election. You’ll need to pass in your database’s connection string when running the ReadySet server and adapter, and have a Consul instance running.

Note: Consul persists data per deployment. Using the same deployment name will carry over any previously installed queries into a new deployment.

Run ReadySet

ReadySet Server

First, compile and run ReadySet server.

cargo run --bin readyset-server --release -- --replication-url <upstream-url>  --deployment <deployment name>

If using the databases supplied by the docker-compose environment in this repository, replace with the URL of the database corresponding to your database engine:

  • MySQL: mysql://root:readyset@127.1/readyset
  • PostgreSQL: postgresql://root:readyset@127.1/readyset

If running with an existing external database, replace with the connection string for that database.

ReadySet MySQL or Postgres Adapter

Then, run the adapter binary corresponding to your upstream database (MySQL or Postgres) The adapter will communicate with servers that have the same deployment name.

MySQL

cargo run --bin readyset-mysql --release -- --upstream-db-url mysql://root:readyset@127.1/readyset  --allow-unauthenticated-connections
  --address 0.0.0.0:3307 --deployment <deployment name>  --prometheus-metrics --query-log --query-log-ad-hoc

Postgres

cargo run --bin readyset-psql --release -- --upstream-db-url postgresql://postgres:readyset@127.1/readyset  --allow-unauthenticated-connections
  --address 0.0.0.0:5433 --deployment <deployment name> --prometheus-metrics --query-log --query-log-ad-hoc

The adapter listens for connections at the address specified in the address flag.

The query-log and query-log-ad-hoc flags ensure that queries are sent to the Prometheus client running in the adapter.

The prometheus-metrics flag exposes an HTTP endpoint in the adapter to allow querying of metrics. This can be reached with an HTTP GET request to :6034/prometheus (e.g., curl -X GET 127.0.0.1:6034/prometheus).

Testing

To run tests for the project, run the following command:

cargo test --skip integration_serial

Testing Notes

Certain tests cannot be run in parallel with others, and these tests are typically in files postfixed with _serial. Running the entire set of tests for a package, i.e. cargo test -p readyset-server may fail if serial tests are included.

Running tests may require increasing file descriptor limits. You can do so by running ulimit -Sn 65535.

Performance

When running ReadySet in a performance-critical setting, make sure you compile with the --release flag.

Note: This repository contains a snapshot of the current state of the ReadySet internal repository. Our team is currently working on getting our live codebase, including all development history, ready to be hosted on GitHub, at which point we'll force push to this repository.

Download details:

Author: readysettech
Source code: https://github.com/readysettech/readyset 
License: View license

#rust #rustlang

What is GEEK

Buddha Community

ReadySet: A Lightweight SQL Caching Engine Written in Rust
Cayla  Erdman

Cayla Erdman

1594369800

Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:

1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.

2. Every database seller needs an approach to separate its item from others.

Right now, contrasts are noted where fitting.

#programming books #beginning sql pdf #commands sql #download free sql full book pdf #introduction to sql pdf #introduction to sql ppt #introduction to sql #practical sql pdf #sql commands pdf with examples free download #sql commands #sql free bool download #sql guide #sql language #sql pdf #sql ppt #sql programming language #sql tutorial for beginners #sql tutorial pdf #sql #structured query language pdf #structured query language ppt #structured query language

Cayla  Erdman

Cayla Erdman

1596441660

Welcome Back the T-SQL Debugger with SQL Complete – SQL Debugger

When you develop large chunks of T-SQL code with the help of the SQL Server Management Studio tool, it is essential to test the “Live” behavior of your code by making sure that each small piece of code works fine and being able to allocate any error message that may cause a failure within that code.

The easiest way to perform that would be to use the T-SQL debugger feature, which used to be built-in over the SQL Server Management Studio tool. But since the T-SQL debugger feature was removed completely from SQL Server Management Studio 18 and later editions, we need a replacement for that feature. This is because we cannot keep using the old versions of SSMS just to support the T-SQL Debugger feature without “enjoying” the new features and bug fixes that are released in the new SSMS versions.

If you plan to wait for SSMS to bring back the T-SQL Debugger feature, vote in the Put Debugger back into SSMS 18 to ask Microsoft to reintroduce it.

As for me, I searched for an alternative tool for a T-SQL Debugger SSMS built-in feature and found that Devart company rolled out a new T-SQL Debugger feature to version 6.4 of SQL – Complete tool. SQL Complete is an add-in for Visual Studio and SSMS that offers scripts autocompletion capabilities, which help develop and debug your SQL database project.

The SQL Debugger feature of SQL Complete allows you to check the execution of your scripts, procedures, functions, and triggers step by step by adding breakpoints to the lines where you plan to start, suspend, evaluate, step through, and then to continue the execution of your script.

You can download SQL Complete from the dbForge Download page and install it on your machine using a straight-forward installation wizard. The wizard will ask you to specify the installation path for the SQL Complete tool and the versions of SSMS and Visual Studio that you plan to install the SQL Complete on, as an add-in, from the versions that are installed on your machine, as shown below:

Once SQL Complete is fully installed on your machine, the dbForge SQL Complete installation wizard will notify you of whether the installation was completed successfully or the wizard faced any specific issue that you can troubleshoot and fix easily. If there are no issues, the wizard will provide you with an option to open the SSMS tool and start using the SQL Complete tool, as displayed below:

When you open SSMS, you will see a new “Debug” tools menu, under which you can navigate the SQL Debugger feature options. Besides, you will see a list of icons that will be used to control the debug mode of the T-SQL query at the leftmost side of the SSMS tool. If you cannot see the list, you can go to View -> Toolbars -> Debugger to make these icons visible.

During the debugging session, the SQL Debugger icons will be as follows:

The functionality of these icons within the SQL Debugger can be summarized as:

  • Adding Breakpoints to control the execution pause of the T-SQL script at a specific statement allows you to check the debugging information of the T-SQL statements such as the values for the parameters and the variables.
  • Step Into is “navigate” through the script statements one by one, allowing you to check how each statement behaves.
  • Step Over is “execute” a specific stored procedure if you are sure that it contains no error.
  • Step Out is “return” from the stored procedure, function, or trigger to the main debugging window.
  • Continue executing the script until reaching the next breakpoint.
  • Stop Debugging is “terminate” the debugging session.
  • Restart “stop and start” the current debugging session.

#sql server #sql #sql debugger #sql server #sql server stored procedure #ssms #t-sql queries

Serde Rust: Serialization Framework for Rust

Serde

*Serde is a framework for serializing and deserializing Rust data structures efficiently and generically.*

You may be looking for:

Serde in action

Click to show Cargo.toml. Run this code in the playground.

[dependencies]

# The core APIs, including the Serialize and Deserialize traits. Always
# required when using Serde. The "derive" feature is only required when
# using #[derive(Serialize, Deserialize)] to make Serde work with structs
# and enums defined in your crate.
serde = { version = "1.0", features = ["derive"] }

# Each data format lives in its own crate; the sample code below uses JSON
# but you may be using a different one.
serde_json = "1.0"

 

use serde::{Serialize, Deserialize};

#[derive(Serialize, Deserialize, Debug)]
struct Point {
    x: i32,
    y: i32,
}

fn main() {
    let point = Point { x: 1, y: 2 };

    // Convert the Point to a JSON string.
    let serialized = serde_json::to_string(&point).unwrap();

    // Prints serialized = {"x":1,"y":2}
    println!("serialized = {}", serialized);

    // Convert the JSON string back to a Point.
    let deserialized: Point = serde_json::from_str(&serialized).unwrap();

    // Prints deserialized = Point { x: 1, y: 2 }
    println!("deserialized = {:?}", deserialized);
}

Getting help

Serde is one of the most widely used Rust libraries so any place that Rustaceans congregate will be able to help you out. For chat, consider trying the #rust-questions or #rust-beginners channels of the unofficial community Discord (invite: https://discord.gg/rust-lang-community), the #rust-usage or #beginners channels of the official Rust Project Discord (invite: https://discord.gg/rust-lang), or the #general stream in Zulip. For asynchronous, consider the [rust] tag on StackOverflow, the /r/rust subreddit which has a pinned weekly easy questions post, or the Rust Discourse forum. It's acceptable to file a support issue in this repo but they tend not to get as many eyes as any of the above and may get closed without a response after some time.

Download Details:
Author: serde-rs
Source Code: https://github.com/serde-rs/serde
License: View license

#rust  #rustlang 

Cayla  Erdman

Cayla Erdman

1596448980

The Easy Guide on How to Use Subqueries in SQL Server

Let’s say the chief credit and collections officer asks you to list down the names of people, their unpaid balances per month, and the current running balance and wants you to import this data array into Excel. The purpose is to analyze the data and come up with an offer making payments lighter to mitigate the effects of the COVID19 pandemic.

Do you opt to use a query and a nested subquery or a join? What decision will you make?

SQL Subqueries – What Are They?

Before we do a deep dive into syntax, performance impact, and caveats, why not define a subquery first?

In the simplest terms, a subquery is a query within a query. While a query that embodies a subquery is the outer query, we refer to a subquery as the inner query or inner select. And parentheses enclose a subquery similar to the structure below:

SELECT 
 col1
,col2
,(subquery) as col3
FROM table1
[JOIN table2 ON table1.col1 = table2.col2]
WHERE col1 <operator> (subquery)

We are going to look upon the following points in this post:

  • SQL subquery syntax depending on different subquery types and operators.
  • When and in what sort of statements one can use a subquery.
  • Performance implications vs. JOINs.
  • Common caveats when using SQL subqueries.

As is customary, we provide examples and illustrations to enhance understanding. But bear in mind that the main focus of this post is on subqueries in SQL Server.

Now, let’s get started.

Make SQL Subqueries That Are Self-Contained or Correlated

For one thing, subqueries are categorized based on their dependency on the outer query.

Let me describe what a self-contained subquery is.

Self-contained subqueries (or sometimes referred to as non-correlated or simple subqueries) are independent of the tables in the outer query. Let me illustrate this:

-- Get sales orders of customers from Southwest United States 
-- (TerritoryID = 4)

USE [AdventureWorks]
GO
SELECT CustomerID, SalesOrderID
FROM Sales.SalesOrderHeader
WHERE CustomerID IN (SELECT [CustomerID]
                     FROM [AdventureWorks].[Sales].[Customer]
                     WHERE TerritoryID = 4)

As demonstrated in the above code, the subquery (enclosed in parentheses below) has no references to any column in the outer query. Additionally, you can highlight the subquery in SQL Server Management Studio and execute it without getting any runtime errors.

Which, in turn, leads to easier debugging of self-contained subqueries.

The next thing to consider is correlated subqueries. Compared to its self-contained counterpart, this one has at least one column being referenced from the outer query. To clarify, I will provide an example:

USE [AdventureWorks]
GO
SELECT DISTINCT a.LastName, a.FirstName, b.BusinessEntityID
FROM Person.Person AS p
JOIN HumanResources.Employee AS e ON p.BusinessEntityID = e.BusinessEntityID
WHERE 1262000.00 IN
    (SELECT [SalesQuota]
    FROM Sales.SalesPersonQuotaHistory spq
    WHERE p.BusinessEntityID = spq.BusinessEntityID)

Were you attentive enough to notice the reference to BusinessEntityID from the Person table? Well done!

Once a column from the outer query is referenced in the subquery, it becomes a correlated subquery. One more point to consider: if you highlight a subquery and execute it, an error will occur.

And yes, you are absolutely right: this makes correlated subqueries pretty harder to debug.

To make debugging possible, follow these steps:

  • isolate the subquery.
  • replace the reference to the outer query with a constant value.

Isolating the subquery for debugging will make it look like this:

SELECT [SalesQuota]
    FROM Sales.SalesPersonQuotaHistory spq
    WHERE spq.BusinessEntityID = <constant value>

Now, let’s dig a little deeper into the output of subqueries.

Make SQL Subqueries With 3 Possible Returned Values

Well, first, let’s think of what returned values can we expect from SQL subqueries.

In fact, there are 3 possible outcomes:

  • A single value
  • Multiple values
  • Whole tables

Single Value

Let’s start with single-valued output. This type of subquery can appear anywhere in the outer query where an expression is expected, like the WHERE clause.

-- Output a single value which is the maximum or last TransactionID
USE [AdventureWorks]
GO
SELECT TransactionID, ProductID, TransactionDate, Quantity
FROM Production.TransactionHistory
WHERE TransactionID = (SELECT MAX(t.TransactionID) 
                       FROM Production.TransactionHistory t)

When you use a MAX() function, you retrieve a single value. That’s exactly what happened to our subquery above. Using the equal (=) operator tells SQL Server that you expect a single value. Another thing: if the subquery returns multiple values using the equals (=) operator, you get an error, similar to the one below:

Msg 512, Level 16, State 1, Line 20
Subquery returned more than 1 value. This is not permitted when the subquery follows =, !=, <, <= , >, >= or when the subquery is used as an expression.

Multiple Values

Next, we examine the multi-valued output. This kind of subquery returns a list of values with a single column. Additionally, operators like IN and NOT IN will expect one or more values.

-- Output multiple values which is a list of customers with lastnames that --- start with 'I'

USE [AdventureWorks]
GO
SELECT [SalesOrderID], [OrderDate], [ShipDate], [CustomerID]
FROM Sales.SalesOrderHeader
WHERE [CustomerID] IN (SELECT c.[CustomerID] FROM Sales.Customer c
INNER JOIN Person.Person p ON c.PersonID = p.BusinessEntityID
WHERE p.lastname LIKE N'I%' AND p.PersonType='SC')

Whole Table Values

And last but not least, why not delve into whole table outputs.

-- Output a table of values based on sales orders
USE [AdventureWorks]
GO
SELECT [ShipYear],
COUNT(DISTINCT [CustomerID]) AS CustomerCount
FROM (SELECT YEAR([ShipDate]) AS [ShipYear], [CustomerID] 
      FROM Sales.SalesOrderHeader) AS Shipments
GROUP BY [ShipYear]
ORDER BY [ShipYear]

Have you noticed the FROM clause?

Instead of using a table, it used a subquery. This is called a derived table or a table subquery.

And now, let me present you some ground rules when using this sort of query:

  • All columns in the subquery should have unique names. Much like a physical table, a derived table should have unique column names.
  • ORDER BY is not allowed unless TOP is also specified. That’s because the derived table represents a relational table where rows have no defined order.

In this case, a derived table has the benefits of a physical table. That’s why in our example, we can use COUNT() in one of the columns of the derived table.

That’s about all regarding subquery outputs. But before we get any further, you may have noticed that the logic behind the example for multiple values and others as well can also be done using a JOIN.

-- Output multiple values which is a list of customers with lastnames that start with 'I'
USE [AdventureWorks]
GO
SELECT o.[SalesOrderID], o.[OrderDate], o.[ShipDate], o.[CustomerID]
FROM Sales.SalesOrderHeader o
INNER JOIN Sales.Customer c on o.CustomerID = c.CustomerID
INNER JOIN Person.Person p ON c.PersonID = p.BusinessEntityID
WHERE p.LastName LIKE N'I%' AND p.PersonType = 'SC'

In fact, the output will be the same. But which one performs better?

Before we get into that, let me tell you that I have dedicated a section to this hot topic. We’ll examine it with complete execution plans and have a look at illustrations.

So, bear with me for a moment. Let’s discuss another way to place your subqueries.

#sql server #sql query #sql server #sql subqueries #t-sql statements #sql

Ruth  Nabimanya

Ruth Nabimanya

1621850444

List of Available Database for Current User In SQL Server

Introduction

When working in the SQL Server, we may have to check some other databases other than the current one which we are working. In that scenario we may not be sure that does we have access to those Databases?. In this article we discuss the list of databases that are available for the current logged user in SQL Server

Get the list of database
Conclusion

#sql server #available databases for current user #check database has access #list of available database #sql #sql query #sql server database #sql tips #sql tips and tricks #tips