Apache Solr vs. Redis Software vs. SAP HANA Cloud

Solr spins up nicely and works effectively for small enterprise environments providing helpful mechanisms for fuzzy searches and facetted searching. For larger enterprises with complex business solutions you'll find the need to hire an expert Solr engineer to optimize the powerful platform to your needs. Internationalization is tricky with Solr and many hosting solutions may limit you to a latin character set.

Incentivized

Redis has been a great investment for our organization as we needed a solution for high speed data caching. The ramp up and integration was quite easy. Redis handles automatic failover internally, so no crashes provides high availability. On the fly scaling scale to more/less cores and memory as and when needed.

Incentivized

I think if you have a large organization, it's probably the product and the marketplace to go to. We're a large management consulting firm operating in four to seven countries. And generally speaking, I think that's the size and the scope where it scales best. I can't speak to smaller companies, but I can't see smaller companies leveraging the benefits as much as a larger organization can.

Incentivized

• Easy to get started with Apache Solr. Whether it is tackling a setup issue or trying to learn some of the more advanced features, there are plenty of resources to help you out and get you going.
• Performance. Apache Solr allows for a lot of custom tuning (if needed) and provides great out of the box performance for searching on large data sets.
• Maintenance. After setting up Solr in a production environment there are plenty of tools provided to help you maintain and update your application. Apache Solr comes with great fault tolerance built in and has proven to be very reliable.

Incentivized

• Easy for developers to understand. Unlike Riak, which I've used in the past, it's fast without having to worry about eventual consistency.
• Reliable. With a proper multi-node configuration, it can handle failover instantly.
• Configurable. We primarily still use Memcache for caching but one of the teams uses Redis for both long-term storage and temporary expiry keys without taking on another external dependency.
• Fast. We process tens of thousands of RPS and it doesn't skip a beat.

Incentivized

• Real-time reporting and analytics on data: because of its in-memory architecture, it is perfect for businesses that need to make quick decisions based on current information.
• Managing workload with complex data: it can handle a vast range of data types, including relational, documental, geospatial, graph, vector, and time series data.
• Developing and deploying intelligent data applications: it provides various tools for such applications and can be used for machine learning and artificial intelligence to automate tasks, gain insights from data, and make predictions.

• These examples are due to the way we use Apache Solr. I think we have had the same problems with other NoSQL databases (but perhaps not the same solution). High data volumes of data and a lot of users were the causes.
• We have lot of classifications and lot of data for each classification. This gave us several problems:
• First: We couldn't keep all our data in Solr. Then we have all data in our MySQL DB and searching data in Solr. So we need to be sure to update and match the 2 databases in the same time.
• Second: We needed several load balanced Solr databases.
• Third: We needed to update all the databases and keep old data status.
• If I don't speak about problems due to our lack of experience, the main Solr problem came from frequency of updates vs validation of several database. We encountered several locks due to this (our ops team didn't want to use real clustering, so all DB weren't updated). Problem messages were not always clear and we several days to understand the problems.

• We had some difficulty scaling Redis without it becoming prohibitively expensive.
• Redis has very simple search capabilities, which means its not suitable for all use cases.
• Redis doesn't have good native support for storing data in object form and many libraries built over it return data as a string, meaning you need build your own serialization layer over it.

Incentivized

• Requires higher processing power, otherwise it won't fly. How ever computing costs are lower. Incase you are migrating to cloud please do not select the highest config available in that series . Upgrading it later against a reserved instance can cost you dearly with a series change
• Lack of clarity on licensing is one major challenge
• Unless S/4 with additional features are enabled mere migration HANA DB is not a rewarding journey. Power is in S/4

Incentivized

We will definitely continue using Redis because: 1. It is free and open source. 2. We already use it in so many applications, it will be hard for us to let go. 3. There isn't another competitive product that we know of that gives a better performance. 4. We never had any major issues with Redis, so no point turning our backs.

Incentivized

We would rate our likelihood of renewing at 9/10. SAP HANA Cloud has proven to be a highly reliable and scalable data platform that consistently delivers strong performance. Its seamless integration with our overall SAP landscape, combined with improved analytics and real-time data capabilities, makes it a core part of our long-term technology strategy.

Incentivized

It takes some time to deploy and currectly maintein it. And also, to learn how to use and integrate in the enviroment as well. Once you get theses steps done, it usability is very simple, and almost of the time it don't require no further attention on it. Even for maintence, if you deploy it on a cluster mode, it is very reliable and easy to take one host down.

Incentivized

It is quite simple to set up for the purpose of managing user sessions in the backend. It can be easily integrated with other products or technologies, such as Spring in Java. If you need to actually display the data stored in Redis in your application this is a bit difficult to understand initially but is possible.

Incentivized

It is very useful solution which provides you speedier data processing, real-time analytics. It helps you manage diverse data types. It also offers you excellent disaster management. It has user friendly interface which helps you navigate system and transactions easily and perform task smoothly.

Incentivized

so far, we didn't get any outage

Incentivized

so far good

Incentivized

The support team has always been excellent in handling our mostly questions, rarely problems. They are responsive, find the solution and get us moving forward again. I have never had to escalate a case with them. They have always solved our problems in a very timely manner. I highly commend the support team.

Incentivized

However, I am not the right person to answer this as we have another department to handle support and contact the service provider for any support required. Although i will say that they are the quick respondent and knows how to handle querry of the customers and provide quick and better support.

Incentivized

Whitelisting of the AWS lambda functions.

Incentivized

Professional GIS people are some of the most risk-averse there are, and it's difficult to get them to move to HANA in one step. Start with small projects building to 80% use of HANA spatial over time.

Incentivized

We tried to use both Elasticsearch and Swiftype with Drupal 8 but there are currently no good modules that integrate Drupal with those solutions. So Solr was really the only option for a Drupal 8 web site. It's not as easy to learn or use as Swiftype, but in the end I think it will be a little less expensive and offer more customization and flexibility.

Incentivized

We are big users of MySQL and PostgreSQL. We were looking at replacing our aging web page caching technology and found that we could do it in SQL, but there was a NoSQL movement happening at the time. We dabbled a bit in the NoSQL scene just to get an idea of what it was about and whether it was for us. We tried a bunch, but I can only seem to remember Mongo and Couch. Mongo had big issues early on that drove us to Redis and we couldn't quite figure out how to deploy couch.

Incentivized

I have deep knowledge of other disk based DBMSs. They are venerable technology, but the attempts to extend them to current architectures belie the fact they are built on 40 year old technology. There are some good columnar in-memory databases but they lack the completeness of capability present in the HANA platform.

Incentivized

I don't have visibility in licensing

Incentivized

Limitation of training deliverable by organization

Incentivized

We are still in process for the first applciaiton

Incentivized

• It has enabled my organization to find information faster by being a one-stop service to search across content that were indexed from varying sources.
• By using synonyms and usual lemmatizations / stemming, it enabled discovery of new content following every search.

Incentivized

• Redis has helped us increase our throughput and server data to a growing amount of traffic while keeping our app fast. We couldn't have grown without the ability to easily cache data that Redis provides.
• Redis has helped us decrease the load on our database. By being able to scale up and cache important data, we reduce the load on our database reducing costs and infra issues.
• Running a Redis node on something like AWS can be costly, but it is often a requirement for scaling a company. If you need data quickly and your business is already a positive ROI, Redis is worth the investment.

Incentivized

• ROI has always been high in terms of the functionality that it offers and the security features it comes with.
• Managing large volumes of data in real-time is not an easy task, but it does it pretty well with faster data processing.

Incentivized