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That supports Java 8 or later. For Datomic On-Prem, you’ll need a server or servers to host the transactor and peers. For Datomic Cloud, you’ll need an AWS account and access to the necessary AWS services. How does Datomic handle data consistency? Datomic provides strong consistency through its use of an ACID-compliant transaction model. All transactions are serialized through a single transactor, ensuring that all reads and writes are consistent across the entire database. Can I migrate my data from Datomic On-Prem to Datomic Cloud? Yes, Datomic provides tools and documentation to help you migrate your data from Datomic On-Prem to Datomic Cloud. The process involves exporting your data from the On-Prem version and importing it into the Cloud version. What kind of support is available for Datomic? Datomic offers a range of support options, including documentation, tutorials, and a community forum. For customers with a paid license, Datomic also offers direct support from its team of experts. How does Datomic handle backups and data recovery? Datomic has built-in support for backups and data recovery. For Datomic On-Prem, you can configure automatic backups to a storage location of your choice. For Datomic Cloud, backups are automatically stored in AWS S3. What programming languages can I use with Datomic? Datomic is designed to work with the Clojure programming language, but it also provides APIs for Java and other JVM languages. Additionally, you can use Datomic’s REST API to interact with the database from any language that can make HTTP requests. How does Datomic handle scaling? Datomic’s architecture allows it to scale horizontally by adding more peers. For Datomic Cloud, scaling is handled automatically by AWS. For Datomic On-Prem, you can add more peers to handle increased read load, and you can add more transactors to handle increased write load.

Not be necessary for the read-only infrastructure, but the Transactor will need some type of sharding mechanism, once it has to deal with heavy loads.● The Datomic Pro pricing is quite restricting, and considering that Datomic Free is no good for any kind of production use, building some experimental projects will be pretty hard for the average developer.Alex Popescu wrote a great post on Datomic, focusing on these critical points and some more.ConclusionI personally think that many of the concepts and ideas behind Datomic, especially making time a first-class citizen, are great and bear a lot of potential. But I can’t see me using it in the near future, because I’d like to prove some of the team’s performance claims for myself, a desire not in keeping with my finances relative to a Pro license.Otherwise, some of Datomic’s advanced features, like fulltext search, multiple data-sources (besides the distributed storage service) and the possibility to use the system for local data-processing only, could potentially be useful.Please share any thoughts you might have in the comments!Dive deeper○ The Datomic Rationale○ A few great videos on Datomic○ Back to the Future with Datomic○ Datomic: Initial Analysis (comparing Datomic to other DBMS)Frequently Asked Questions (FAQs) about DatomicWhat is the difference between Datomic On-Prem and Datomic Cloud? Datomic On-Prem and Datomic Cloud are two different deployment models for the Datomic database system. Datomic On-Prem is designed to be installed and run on your own servers or in a data center of your choice. It provides you with full control over your data and infrastructure. On the other hand, Datomic Cloud is a fully managed service that runs on AWS (Amazon Web Services). It provides automatic scaling, backups, and failover, reducing the operational overhead. How is Datomic priced? Datomic has different pricing models for its On-Prem and Cloud versions. For Datomic On-Prem, you pay a one-time license fee based on the number of peers and transactors. For Datomic Cloud, you pay based on the AWS resources consumed, which can vary depending on your usage and the specific AWS services you use. Is there a free version of Datomic? Yes, Datomic offers a free version known as Datomic Free. It’s a fully functional version of Datomic that’s limited to a single transactor and two peers. It’s ideal for development, testing, and small production deployments. What are the system requirements for Datomic? Datomic can run on any system

Own local cache of data segments, in memory”, which would require perfect cache synchronisation. Otherwise consistency is only guaranteed for one peer, which quite frankly is pointless. Hopefully, this management overhead won’t neutralize the promising ACID capabilities and the performance gains of in-memory operations.The second quote makes initial sense, but still raises a few concerns:What happens when one Transactor faces too much load? The Datomic team would like to avoid sharding, but wouldn’t exactly that be necessary at some point? Also, even if we pretend that the number of transactions wouldn’t increase with more peers, the time it takes to transmit changes to all of them sure does.In conclusion, the Transactor could be an amazing thing to have with smaller datasets but may become a potential performance bottleneck or Single Point of Failure.Storage servicesThese services handle the distributed storage of data. Some possibilities:● Transactor-Local storage (free, useful for playing with Datomic on a single machine)● SQL Databases (require Datomic Pro)● DynamoDB (require Datomic Pro)● Infinispan Memory Cluster (require Datomic Pro)… plus a few more. Storage service support could be one big reason to try out Datomic, but unfortunately, only the temporary local storage is available for Datomic Free users (aka users who aren’t willing to pay $3,000+ for a brand new DBMS).All in all, the Datomic architecture comes with loads of innovative ideas and potential benefits, but its real-world applicability remains to be proven.Please read the detailed architecture overview in the Datomic documentation or watch this 20 minute video by Mr. Hickey himself.(Original image taken from here.)2. A fact-based data-modelDatomic doesn’t model data as documents, objects or rows in a table. Instead, data is represented as immutable facts called “Datoms”. They are made up of four pieces:EntityAttributeValueTransaction timestampDatoms are highly reminiscent of the subject-predicate-object scheme used in RDF Triplestores.Anything can be a datom:“John’s balance is $12,000” → [john :balance 12000 ]These attribute definitions are the only type of schema implied on the dataset.In a relational database, this would be represented as a 12000 at the “john”-row in the “balance” cell (data is place-oriented). If now, a month later, John’s balance changes to 6,000, this specific cell would be wiped, and the new value would be put in. The fact, that John had 12,000 on his account a month ago is gone forever.One of the main reasons for the creation of Datomic was the feeling, that today’s hardware is finally able

A subset of Datomic functionalityand should work as a drop-in replacement on the JVM. The rest of Datahike willbe ported to core.async to coordinate IO in a platform-neutral manner.Refer to the docs for more information:backend developmentbenchmarkinggarbage collectioncontributing to Datahikeconfigurationdifferences to Datomicentity speclogging and error handlingschema flexibilitytime varianceversioningFor simple examples have a look at the projects in the examples folder.Example ProjectsInvoice creationdemonstrated at the Dutch ClojureMeetup.Relationship to Datomic and DataScriptDatahike provides similar functionality to Datomic and canbe used as a drop-in replacement for a subset of it. The goal of Datahike is notto provide an open-source reimplementation of Datomic, but it is part of thereplikativ toolbox aimed to build distributeddata management solutions. We have spoken to many backend engineers and Clojuredevelopers, who tried to stay away from Datomic just because of its proprietarynature and we think in this regard Datahike should make an approach to Datomiceasier and vice-versa people who only want to use the goodness of Datalog insmall scale applications should not worry about setting up and depending onDatomic.Some differences are:Datahike runs locally on one peer. A transactor might be provided in thefuture and can also be realized through any linearizing write mechanism, e.g.Apache Kafka. If you are interested, please contact us.Datahike provides the database as a transparent value, i.e. you can directlyaccess the index datastructures (hitchhiker-tree) and leverage theirpersistent nature for replication. These internals are not guaranteed to staystable, but provide useful insight into what is going on and can be optimized.Datahike supports GDPR compliance by allowing to completely remove database entries.Datomic has a REST interface and a Java APIDatomic provides timeoutsDatomic is a full-fledged scalable database (as a service) built from theauthors of Clojure and people with a lot of experience. If you need this kindof professional support, you should definitely stick to Datomic.Datahike's query engine and most of its codebase come fromDataScript. Without the work onDataScript, Datahike would not have been possible. Differences to Datomic withrespect to the query engine are documented there.When to Choose Datahike vs. Datomic vs. DataScriptDatahikePick Datahike if your app has modest requirements towards a typical durabledatabase, e.g. a single machine and a few millions of entities at maximum.Similarly, if you want to have an open-source solution and be able to study andtinker with the codebase of your database, Datahike provides a comparativelysmall and well composed codebase to tweak it to your needs. You should alsoalways be able to migrate to Datomic later easily.DatomicPick Datomic if you already know that you will need scalability later or if youneed a network API for your database. There is also plenty of material aboutDatomic online already. Most of it applies in some form or another to Datahike,but it might be easier to use Datomic directly when you first learn Datalog.DataScriptPick DataScript if you want the fastest possible query performance and do nothave a huge amount of data. You can easily persist the write operationsseparately and use the fast in-memory index data structure of DataScript then.Datahike also at the moment does not support ClojureScript anymore, although weplan

Architecture. Datomic puts the brain of your app back into the client. In a traditional setup, the server handles everything from queries and transactions to actually storing the data. With increasing load, more servers are added and the dataset is sharded across these. As most of todays NoSQL databases show, this method works very well, but comes at the cost of some “brain”, as Mr. Hickey argues. The loss of consistency and/or query-power is a well-known tradeoff for scale.To achieve distributed storage, but with a powerful query language and consistent transactions, Datomic leverages existing scalable databases as simple distributed storage services. All the complex data processing is handled by the application itself. Almost as in a native desktop application (if you can remember one of those).This brings us to the first cornerstone of the Datomic infrastructure:The Peer ApplicationA peer is created by embedding the Datomic library into your client-code. From then, every instance of your application will be able to:● communicate with the Transactor and storage services● run Datalog queries, access data and handle caching of the working setEvery peer manages its own working-set of data in memory and synchronizes with a “Live Index” of the global dataset. This allows the application to run very flexible queries without the need for roundtrips (more on that under “Criticism”).But so far, we’ve only got back query power. To also re-enable consistent transactions, Datomic takes a step further: it makes the storage service read-only and forces all writes through a new kind of architectural component, the “Transactor”.The TransactorThe Transactor will:● handle ACID transactions● synchronously write to redundant storage● communicate changes to Peers● indexing your dataset in the backgroundIt seems as if the Datomic Team banished everything that made Relational DBMS hard to scale in a separate module, and tried not to worry too hard about it. For example, the Datomic-Rationale states:“When reads are separated from writes, writes are never held up by queries. In the Datomic architecture, the transactor is dedicated to transactions, and need not service reads at all!”and“Putting query engines in peers makes query capability as elastic as the applications themselves. In addition, putting query engines into the applications themselves means they never wait on each other’s queries.”The first statement is true for some read-operations, but I couldn’t find a hint as to how the Transactor handles reads in transactions. Though it is mentioned that “Each peer and transactor manages its