{"action":"created","comment":{"url":"https://api.github.com/repos/arangodb/arangodb/pulls/comments/234742383","pull_request_review_id":176438489,"id":234742383,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIzNDc0MjM4Mw==","diff_hunk":"@@ -0,0 +1,141 @@\n+Data Modeling and Operational Factors\n+====================================\n+\n+Design the data model of your application is a crucial task that can make or break the performance\n+of your application. A well-designed data model will allow you to write efficient AQL queries, increase\n+throughput of CRUD operations and will make sure your data is distributed in the most effective way.\n+\n+Whether you design a new application with ArangoDB or port an existing one to use ArangoDB, you should\n+always analyze the (expected) data access patterns of your application in conjunction with several factors:\n+\n+Operation Atomicity\n+-------------------\n+\n+All insert / update / replace / remove operations in ArangoDB are atomic on a _single_ document.\n+Using a single instance of ArangoDB, multi-document / multi-collection queries are guaranteed to be fully ACID, however\n+in cluster mode only single-document operations are also fully ACID. This has implications if you try to ensure\n+consistency across multiple operations.\n+\n+### Denormalizing Data\n+\n+In traditional _SQL_ databases it is considered a good practice to normalize all your data across multiple tables\n+to avoid duplicated data and ensure consistency.\n+\n+ArangoDB is a schema-less _NoSQL_ document database so a good data model is not necessarily normalized. On the contrary to\n+avoid extra joins it is often an advantage to deliberately _denormalize_ your datamodel.\n+\n+To denormalize your data model you essentially combine all related entities into a single document instead of spreading it\n+over multiple documents and collections. The advantage of this is that it allows you to atomically update all of your\n+connected data, the downside is that your documents become larger (see below for more considerations on large documents).\n+\n+As a simple example, lets say you want to maintain the balance of a shopping basket (from an online shop) together with a list of all included items and prices.\n+The total balance of all items in the shopping basket should stay in sync with the contained items, then you may put\n+all contained items inside the shopping basket document and only update them together:\n+\n+```json\n+{\n+    \"_id\": \"basket/123\",\n+    \"_key\": \"123\",\n+    \"_rev\": \"_Xv0TA0O--_\",\n+    \"user\": \"some_user\",\n+    \"balance\": \"100\",\n+    \"items\": [ { \"price\": 10, \"title\": \"Harry Potter and the Philosopher’s Stone\" }, \n+               { \"price\": 90, \"title\": \"Vacuum XYZ\" } ]\n+}\n+```\n+\n+### Ensuring Consistent Atomic Updates\n+\n+There are ways to ensure atomicity and consistency when perfoming updates in your application.\n+ArangoDB allows you to specify the revision ID of the existing document you would like to update.\n+This way you can ensure that if your application has read a document with a certain revision, the\n+modifications to it are only allowed to pass if and only if the document was not changed by someone else.\n+\n+You can specify the revision via the `_rev` field inside the document or via the `If-Match: <revision>`\n+header in the documents REST API. In the _arangosh_ you can peform such an operation like this:\n+\n+```js\n+db.basketCollection.update({\"_key\": \"123\", \"_rev\": \"_Xv0TA0O--_\"}, data)\n+// or replace\n+db.basketCollection.replace({\"_key\": \"123\", \"_rev\": \"_Xv0TA0O--_\"}, data)\n+```\n+\n+Indexes\n+-------\n+\n+Indexes can improve the performance of AQL queries drastically. Queries that frequently filter on one more fields\n+can be made faster by creating an index via the _ensureIndex_ command. There is already a permanent primary\n+index in every collection on the *_key* and *_id* fields as well as the edge index on *_from* and *_to* (for edge collections).\n+\n+Should you decide to create an index you should consider a few things:\n+\n+- Indexes are a trade-off between storage space and query speed.\n+- Each new index will increase the amount of RAM and (for the rocksdb storage) the amount of disk space needed.\n+- Indexes with [indexed array values](../Indexing/IndexBasics.md#indexing-array-values) need an extra index entry per array entry\n+- Consider the extra space requirements of indexes when planning server capacities.\n+- Adding indexes increases the write-amplification i.e. it negatively affects the write performance\n+- Each index needs to add at least one index entry per document. You can use _sparse indexes_ to avoid adding index entries for unused fields\n+- Collections that are more frequently read benefit the most from added indexes, provided you actually use the indexes\n+- Collections with a high rate of inserts or updates compared to reads may hurt overall performance.\n+\n+Generally it is best to design your indexes with your queries in mind. Use the [query profiler](../../AQL/ExecutionAndPerformance/QueryProfiler.html) to understand the bottlenecks in your queries. For more information on indexes see [Index Basics](../Indexing/IndexBasics.md)","path":"Documentation/Books/Manual/DataModeling/OperationalFactors.md","position":81,"original_position":81,"commit_id":"2ce3e8046cb03c694e56a93e27fae00ecdb72b56","original_commit_id":"2ce3e8046cb03c694e56a93e27fae00ecdb72b56","user":{"login":"Simran-B","id":7819991,"node_id":"MDQ6VXNlcjc4MTk5OTE=","avatar_url":"https://avatars3.githubusercontent.com/u/7819991?v=4","gravatar_id":"","url":"https://api.github.com/users/Simran-B","html_url":"https://github.com/Simran-B","followers_url":"https://api.github.com/users/Simran-B/followers","following_url":"https://api.github.com/users/Simran-B/following{/other_user}","gists_url":"https://api.github.com/users/Simran-B/gists{/gist_id}","starred_url":"https://api.github.com/users/Simran-B/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Simran-B/subscriptions","organizations_url":"https://api.github.com/users/Simran-B/orgs","repos_url":"https://api.github.com/users/Simran-B/repos","events_url":"https://api.github.com/users/Simran-B/events{/privacy}","received_events_url":"https://api.github.com/users/Simran-B/received_events","type":"User","site_admin":false},"body":"~80 chars and one link per line\r\n\r\n```suggestion\r\nGenerally it is best to design your indexes with your queries in mind.\r\nUse the [query profiler](../../AQL/ExecutionAndPerformance/QueryProfiler.html)\r\nto understand the bottlenecks in your queries. For more information on\r\nindexes see [Index Basics](../Indexing/IndexBasics.md)\r\n```","created_at":"2018-11-19T19:01:57Z","updated_at":"2018-11-19T19:30:53Z","html_url":"https://github.com/arangodb/arangodb/pull/7374#discussion_r234742383","pull_request_url":"https://api.github.com/repos/arangodb/arangodb/pulls/7374","author_association":"CONTRIBUTOR","_links":{"self":{"href":"https://api.github.com/repos/arangodb/arangodb/pulls/comments/234742383"},"html":{"href":"https://github.com/arangodb/arangodb/pull/7374#discussion_r234742383"},"pull_request":{"href":"https://api.github.com/repos/arangodb/arangodb/pulls/7374"}}},"pull_request":{"url":"https://api.github.com/repos/arangodb/arangodb/pulls/7374","id":232052555,"node_id":"MDExOlB1bGxSZXF1ZXN0MjMyMDUyNTU1","html_url":"https://github.com/arangodb/arangodb/pull/7374","diff_url":"https://github.com/arangodb/arangodb/pull/7374.diff","patch_url":"https://github.com/arangodb/arangodb/pull/7374.patch","issue_url":"https://api.github.com/repos/arangodb/arangodb/issues/7374","number":7374,"state":"open","locked":false,"title":"Documentation 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