Yes, a query like the one you've provided can be cached in Redis. However, there are some nuances to consider when caching queries, especially dynamically generated queries like this one, which include parameters like latitude, longitude, and others.
Caching in Redis can help avoid repeatedly running the same complex query in SQL Server, but you must ensure that:
- Parameters are handled properly to generate a unique cache key.
- You define a cache expiration policy to avoid serving stale data.
- Redis should cache query results based on query parameters (such as
@destination values in this case).
Steps to Cache This Query in Redis
Here’s how you would cache this specific query using Node.js with Redis:
1. Generate a Unique Cache Key
Since the query includes dynamic parameters like the destination's latitude and longitude, it is essential to generate a unique cache key for each combination of these values. The cache key can be based on:
- Latitude and Longitude: These parameters define the geographical point.
- Query: The structure of the query (e.g., filtering conditions).
A unique cache key could be generated by concatenating these parameters into a string and hashing them.
2. Retrieve Data from Redis (Cache Hit)
Before executing the SQL query on SQL Server, check Redis for a cached result using the generated cache key.
3. If Cache Miss, Execute the Query on SQL Server and Cache the Result
If the result is not available in Redis, execute the query on SQL Server, get the result, and then store the result in Redis using the same cache key.
Example: Implementing Caching for Your Query
Here’s how you can implement this using Node.js, Redis, and MSSQL:
require('dotenv').config();
const redis = require('redis');
const sql = require('mssql');
const crypto = require('crypto'); // To generate a unique cache key
// Redis Client Setup
const redisClient = redis.createClient({
host: process.env.REDIS_HOST,
port: process.env.REDIS_PORT
});
// MSSQL Client Setup
const sqlConfig = {
user: process.env.MSSQL_USER,
password: process.env.MSSQL_PASSWORD,
database: process.env.MSSQL_DATABASE,
server: process.env.MSSQL_SERVER,
port: parseInt(process.env.MSSQL_PORT, 10),
options: {
encrypt: false, // For Azure, set to true if using SSL
trustServerCertificate: true
}
};
// Generate a unique cache key based on the query and parameters (latitude, longitude)
function generateCacheKey(latitude, longitude) {
const keyString = `lat:${latitude}_long:${longitude}`;
return crypto.createHash('sha256').update(keyString).digest('hex');
}
// Execute the query with caching logic
async function executeQueryWithCache(latitude, longitude, cacheExpiry = 60) {
const cacheKey = generateCacheKey(latitude, longitude); // Unique cache key
return new Promise((resolve, reject) => {
// Step 1: Check Redis cache
redisClient.get(cacheKey, async (err, cachedData) => {
if (err) {
console.error('Error fetching from Redis:', err);
return reject(err);
}
if (cachedData) {
// Cache hit: return cached data
console.log('Cache hit! Data from Redis:', JSON.parse(cachedData));
return resolve(JSON.parse(cachedData));
} else {
// Cache miss: Execute query in MSSQL
const query = `
DECLARE @destination geography = geography::Point(${latitude}, ${longitude}, 4326);
SELECT
(SELECT Priority FROM MerchantPlanTypes WHERE PlanType = Merchants.PlanType) AS plan_priority,
Name AS name, MerchantID AS merchant_id, IsVerified AS is_verified, State AS state,
Member_Dir_Name AS member_dir_name, Company_Name AS company_name, Address_Line_1 AS address_line_1,
Address_Line_2 AS address_line_2, Address_Line_3 AS address_line_3, Image_URL AS image_url,
Phone_No AS phone_no, PlanType AS plan_type, Latitude AS lat, Longitude AS long, CreatedBy AS created_by,
CreatedAt AS created_at, Description AS description, Opening_Hours AS opening_hours,
(SELECT TOP 1 URL AS url FROM MerchantsAssets WHERE MerchantID = Merchants.MerchantID AND Type = 'CompanyLogo') AS company_logo,
Status AS status,
CAST([Location].STDistance(@destination) / 1000 AS Decimal(18, 2)) AS distance,
Nature_of_Business AS cat_code,
ISNULL((SELECT COUNT(1) FROM Endorsement WHERE ObjectID = Merchants.MerchantID AND ObjectType = 'Merchant' AND EndorseStatus = 1), 0) AS endorse_count,
ISNULL((SELECT COUNT(1) FROM Likes WHERE ObjectType = 'Merchant' AND ObjectID = Merchants.MerchantID AND LikeStatus = 1), 0) AS likes_count,
ISNULL((SELECT COUNT(1) FROM ReviewRating WHERE ObjectType = 'Merchant' AND ObjectID = Merchants.MerchantID), 0) AS review_rating_count,
ISNULL((SELECT ISNULL(AVG(Rating), 0) AS RatingAvg FROM ReviewRating WHERE ObjectType = 'Merchant' AND ObjectID = Merchants.MerchantID), 0) AS rating_avg,
(SELECT Description FROM ProppyCaresCategory WHERE Nature_of_Business = Code) AS category_name,
(SELECT mg.GroupCode FROM MerchantsGroups AS mg INNER JOIN MerchantsCategoryGrouping AS mcg ON mcg.GroupCode = mg.GroupCode WHERE mcg.CategoryCode = Nature_of_Business) AS group_code
FROM Merchants
WHERE MerchantID <> '' AND MerchantID IS NOT NULL AND Latitude IS NOT NULL AND Longitude IS NOT NULL
AND (Status = 4 OR (Status = 1 AND StatusPaid = 1) OR Status = 8)
AND StateID = (SELECT ID FROM STATES WHERE Name = 'Kuala Lumpur')
AND [Location].STDistance(@destination) BETWEEN 5010 AND 20000
ORDER BY distance ASC, plan_priority DESC, endorse_count DESC
OFFSET 0 ROWS FETCH NEXT 20 ROWS ONLY
`;
try {
const pool = await sql.connect(sqlConfig);
const result = await pool.request().query(query);
// Cache the result in Redis with an expiration time
redisClient.setex(cacheKey, cacheExpiry, JSON.stringify(result.recordset));
console.log('Data from MSSQL:', result.recordset);
pool.close();
return resolve(result.recordset);
} catch (sqlErr) {
console.error('MSSQL query error:', sqlErr);
return reject(sqlErr);
}
}
});
});
}
// Example function to run the query with caching
async function fetchData() {
const latitude = 3.2119882;
const longitude = 101.636992;
try {
const data = await executeQueryWithCache(latitude, longitude);
console.log('Final data:', data);
} catch (error) {
console.error('Error:', error);
}
}
// Run the query
fetchData();
Code Breakdown:
generateCacheKey(latitude, longitude):
- This function creates a unique cache key based on the latitude and longitude provided in the query.
- It uses
crypto.createHash('sha256') to create a hash from the concatenated latitude and longitude values, ensuring that the cache key is unique for each geographical point.
Redis get() and setex():
redisClient.get(cacheKey, callback): This checks Redis to see if the result for the given cache key is already stored.redisClient.setex(cacheKey, expiry, data): This stores the result in Redis, with an expiration time defined (e.g., 60 seconds).
MSSQL Query:
- The query dynamically injects the latitude and longitude into the
DECLARE @destination geography line.
- The results are fetched from MSSQL if the cache is missed, and the result is stored in Redis for future requests.
Cache Expiry:
- The cache expiration time is set to 60 seconds by default, but you can modify this based on your application needs by changing the
cacheExpiry value passed to setex().
How the Flow Works:
Redis Cache Check (Cache Hit):
- When the query with the same latitude and longitude is requested again, Redis will return the cached result, avoiding hitting the MSSQL server.
Redis Cache Miss:
- If Redis doesn’t have a cached result for the provided
latitude and longitude, the system will execute the query against the MSSQL database, store the result in Redis, and return it.
Conclusion:
Yes, you can cache SQL queries like the one you provided in Redis, especially when it involves complex geography calculations or large datasets. The key is to generate a unique cache key based on query parameters (in this case, the latitude and longitude) and store the results in Redis for a defined period.
This approach will help you reduce load on your MSSQL server and improve performance by serving repeated queries directly from the Redis cache.
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