![]() In this case, chances of a write loss are thus 50% due to the 100ms window where the journal might not be flushed to disk. The acknowledgment in this case is guaranteed but not 100% it has reached disk journal. When the write concern is set high, it almost takes double the time to return with the execution time being 0.027ms min and 0.031ms max. ![]() In this case, the basic acknowledgment of write is disabled but one can still get information regarding socket exceptions and any network error that may have been triggered. In our test, the write concern set to low resulted in the query being executed in min of 0.013ms and max of 0.017ms. An acknowledgment is simply a receipt that the server accepted the write to process. On the other hand, if the write concern is high, there is an error handling prompt and thus the writes will be acknowledged. A limitation in this case will be, you won’t be sure if these writes were successful. A simple explanation for this is that when the value is low then you are not concerned about the possibility of losing some writes in an event of mongod crash, network error or anonymous system failure. On the other hand, if the value is set high, then the write calls are slow and consequently increase on the query latency. For a high throughput operation, if this value is set to low then the write calls will be so fast thus reduce the latency of the request. Write concern describes acknowledgment level requested from MongoDB for write operations in this case to a standalone MongoDB. We ran the insert commands below db.getCollection('location').insertMany(,) Write Concern We are using MongoDB version 4.0 on an Ubuntu Linux 12.04 Intel Xeon-SandyBridge E3-1270-Quadcore 3.4GHz dedicated server with 32GB RAM, Western Digital WD Caviar RE4 1TB spinning disk and Smart XceedIOPS 256GB SSD. The file has got more than 500 documents which are quite enough for our test. Our benchmark test will involve some big location data which can be downloaded from here and we will be using Robo3t software to manipulate our data and collect the information we need. When the test workload is large, it will help you predict future expectations of your database performance hence start some capacity planning early enough. Employ data volumes that are a representation of “big data” datasets which will certainly exceed the RAM capacity for an individual node.This is to improve on data integrity by ensuring the data is consistent and is most applicable especially in the production environment. Always ensure that all data writes were done in a manner that allowed no data loss. When running a benchmark you should therefore use data which is a clear presentation of your application. It is not quite easy to work with this data with default or rather sub-standard database configurations as it may escalate to issues like poor latency and poor throughput operations involving the complex data. This is to say, data presentation has also changed with time for example storing simple fields to objects and arrays. Modern applications are becoming more complex every day and this is transmitted down to the data structures. Select workloads that are a typical representation of today’ modern applications.After realizing a rising trend of data, you need to do some benchmarking on how you will meet the requirements of this vast growing data. Besides, applications grow with time in terms of users and probably the amount of data that is to be served hence need to do some capacity planning before this time. The idea behind benchmarking is to get a general idea on how different configuration options affect performance, how you can tweak some of these configurations to get maximum performance and estimate the cost of improving this implementation. As much as you may also get impressive figures from the benchmark process, you need to be cautious as this may be a different case when running your database with real queries. MongoDB does not have a standard benchmarking methodology thereby we need to resolve in testing queries on own hardware. Benchmarking is basically running some set of queries with some test data along with some resource provision to determine whether these parameters meet the expected performance level. You may ask how can you tell if the database is really going to have an issue while it is working normally? Well, that is what we are going to discuss in this article and we term it as benchmarking. Every organization involving them therefore has the mandate to ensure smooth performance of these DBMs through consistent monitoring and handling minor setbacks before they escalate into enormous complications that may result in an application downtime or slow performance. ![]() Database systems are crucial components in the cycle of any successful running application.
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