Apache Solr vs. Google Cloud Operations Suite

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

Easy to set up in all environments. With this tool, the company is now able to measure both well-functioning data and data that needs immediate intervention. This early detection facilitates decisions about actions to correct the disorder and improve indexes. Stackdriver is now essential for the company's security and monitoring team and we plan to expand to other branches.

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.

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• All the application error reporting available at a single place.
• You will get the real-time application error and alerts.
• It's really easy to use and no need to worry for the setup.

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• 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.

• For searching and filtering the logs the system is slow sometimes.
• An agent needs to be installed in each VM to monitor specific metrics.
• The pricing model is confusing.

Incentivized

It is also a great problem detection tool, and this is extremely important for General Motors.

The user can count on the ease of flexible panels and advanced visualization tools that help to identify problems. Among the most common, we can mention:

- containment of hosts;

- cloud provider limitation;

- hardware wear.

And also Stackdriver Integration with other Google Cloud data tools such as BigQuery, Cloud Pub/Sub, Cloud Storage and Cloud Database.

Incentivized

Apache Solr is a ready-to-use product addressing specific use cases such as keyword searches from a huge set of data documents.

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Incentivized

• Improved response time in e-commerce websites.
• Developer's job is easier with Apache Solr in use.
• Customization in filtering and sorting is possible.

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• We completely depends on Stackdriver for error monitoring and reporting.
• Stackdriver is very simple to use and available at default.
• Stackdriver is the key part for our business to get the insight of our application behavior.

Incentivized