![]() Redis’ FAQ has more details on the actual memory use required of a few simple examples, but for developers and database administrators, the pros and cons inherent in Redis are worth considering. The downside of running in memory is, of course, that as your database size increases, so to does your memory usage. This means that, unlike the disk-based storage of a system like MongoDB, Redis is extremely fast. Perhaps the biggest advantage Redis has over other NoSQL systems is that runs almost entirely in memory. If you want to discover how Redis can guarantee you real-time data transmission, then make sure to contact us.Redis has grown to become one of the most popular NoSQL database systems (not to mention cache systems) in use today. Going one step further, you may want to find out Why Your SQL Server Needs Redis in our new whitepaper.īut regarding real-time data with Redis, this is only one of many ways you can use it to provide real-time experiences. Now you can also use your Redis client to access this data as Redis data structures: 127.0.0.1:6379> keys product*ġ) "product:2e3f8611dbe94a588706a2aaea547caa"Ī more effective approach would be to use the scan command because it allows you to paginate as you navigate through the data. Scala> val results = sql("select * from products") Scala> sql("insert into products values = ('10200','ZXYW','Description of ZXYW', 300)") Scala> sql("create table if not exists products(id string, name string, description string, price int) using .redis options (table 'product')") Here’s an example of how you can do this: $ spark-shell -jars spark-redis-2.3.1-SNAPSHOT-jar-with-dependencies.jar scala> import .SparkSession You can also run your queries on Apache Spark engine. You then next have to make sure that you have your Redis instance running. In our example, we’ll run Redis on localhost and the default port 6379. For example, with spark-redis 2.3.1, you get spark-redis-2.3.1-SNAPSHOT-jar-with- dependencies.jar. The stack for Spark SQL and Redisįirst, you need to download spark-redis and build the library to get the jar file. In other words, you can insert, update and query data using SQL commands, but the data is internally mapped to Redis data structures.įigure 2. Spark-Redis library allows you to use the DataFrame APIs to store and access Redis data. Now, if you want to maintain the SQL interface in your solutions and only change the underlying data store to Redis to make it faster, then you can do so by using Apache Spark and Spark-Redis library. HGETALL product:10003 Use DataFrames to automatically map your tables to Redis data structures Select * from Product where price < 300 Redis: Select * from Products where id = 10200 Redis: ![]() HMSET product:10200 name ZXYW desc “Description for ZXYW” price 300 Insert into Products (id, name, description, price) Below are some examples of SQL and Redis equivalent commands: With this option, you need to make changes to your code to use Redis queries instead of SQL commands. But before we go any further, we have a Redis Hackathon contestant who created his own app that allows you to query data in Redis with SQL.įigure 1. There are a number of ways you can run a Redis query and introduce Redis into your architecture without disrupting your current SQL-based solution. Now, if you’re in the same situation as my friend, we have good news for you. Can Redis help here? Keep in mind that we can’t rip and replace our SQL-based solution all at once. However, sometimes it takes minutes to get the results. We have use cases where we need to record data and perform analytical operations in real-time. “ We have a pain point with our data warehousing solutions. We began talking about Redis’ queries once he explained a problem that he was facing. I actually raised this point a few years ago when speaking to a friend who manages data warehousing solutions at a retail company. Running a Redis SQL query doesn’t have to be difficult. Related E-Book download: The Importance of In-Memory NoSQL Databases
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