如何在ArangoDB 2.7中提高检索查询性能

Question

I am beginner in python and ArangoDB. I have strored the data in ArangoDB on Single colletion name "DSP". My query is :

for k in 
    (for t in DSP return [t.data])
        for z in k
           for p in z
              filter p.name == "name" || 
                     p.content == "pdf" ||
                     p.content == "xml" ||
                     p.name == "Book"
              return p

and the json data which in have stored: in in the format like

{"data": [{"content": "Java", "type": "string", "name": "name", "key": 1}, {"content": "D:/Java", "type": "string", "name": "location", "key": 1}, {"content": "File folder", "type": "string", "name": "type", "key": 1}, {"content": 1896038645, "type": "int", "name": "size", "key": 1}, {"content": 7, "type": "string", "name": "child_folder_count", "key": 1}, {"content": 7, "type": "string", "name": "child_file_count", "key": 1}, {"content": "parse_dir.py", "type": "string", "name": "name", "key": 101}, {"content": "D:/Java/parse_dir.py", "type": "string", "name": "location", "key": 101}, {"content": "py", "type": "string", "name": "mime-type", "key": 101}, {"content": 4032, "type": "string", "name": "size", "key": 101}, {"content": "Wed Dec 30 21:36:32 2015", "type": "string", "name": "created_date", "key": 101}, {"content": "Wed Dec 30 21:42:38 2015", "type": "string", "name": "modified_date", "key": 101}, {"content": "result.json", "type": "string", "name": "name", "key": 102}, {"content": "D:/Java/result.json", "type": "string", "name": "location", "key": 102}, {"content": "json", "type": "string", "name": "mime-type", "key": 102}, {"content": 1134450, "type": "string", "name": "size", "key": 102}, {"content": "Wed Dec 30 21:36:45 2015", "type": "string", "name": "created_date", "key": 102}, {"content": "Wed Dec 30 21:36:45 2015", "type": "string", "name": "modified_date", "key": 102}, {"content": "rmi1.rar", "type": "string", "name": "name", "key": 103}, {"content": "D:/Java/rmi1.rar", "type": "string", "name": "location", "key": 103}, {"content": "rar", "type": "string", "name": "mime-type", "key": 103}, {"content": 165116, "type": "string", "name": "size", "key": 103}, {"content": "Sun Aug 25 07:29:52 2013", "type": "string", "name": "created_date", "key": 103}, {"content": "Tue Aug 30 16:18:34 2011", "type": "string", "name": "modified_date", "key": 103}, {"content": "servlet.rar", "type": "string", "name": "name", "key": 104}, {"content": "D:/Java/servlet.rar", "type": "string", "name": "location", "key": 104}, {"content": "rar", "type": "string", "name": "mime-type", "key": 104}, {"content": 782, "type": "string", "name": "size", "key": 104}, {"content": "Sun Aug 25 07:29:52 2013", "type": "string", "name": "created_date", "key": 104}, {"content": "Tue Aug 30 16:18:30 2011", "type": "string", "name": "modified_date", "key": 104}, {"content": "crawler projects", "type": "string", "name": "name", "key": 2}, {"content": "D:/Java/crawler projects", "type": "string", "name": "location", "key": 2}, {"content": "File folder", "type": "string", "name": "type", "key": 2}, {"content": 1886842316, "type": "int", "name": "size", "key": 2}, {"content": 5, "type": "string", "name": "child_folder_count", "key": 2}, {"content": 5, "type": "string", "name": "child_file_count", "key": 2}, {"content": ".metadata", "type": "string", "name": "name", "key": 3}, {"content": "D:/Java/crawler projects/.metadata", "type": "string", "name": "location", "key": 3}, {"content": "File folder", "type": "string", "name": "type", "key": 3}, {"content": 10131546, "type": "int", "name": "size", "key": 3}, {"content": 2, "type": "string", "name": "child_folder_count", "key": 3}, {"content": 2, "type": "string", "name": "child_file_count", "key": 3}, {"content": ".lock", "type": "string", "name": "name", "key": 301}, {"content": "D:/Java/crawler projects/.metadata/.lock", "type": "string", "name": "location", "key": 301}, {"content": "", "type": "string", "name": "mime-type", "key": 301}, {"content": 0, "type": "string", "name": "size", "key": 301}, {"content": "Sun Aug 25 07:29:52 2013", "type": "string", "name": "created_date", "key": 301}, {"content": "Mon May 30 12:21:45 2011", "type": "string", "name": "modified_date", "key": 301}, {"content": ".log", "type": "string", "name": "name", "key": 302}, {"content": "D:/Java/crawler projects/.metadata/.log", "type": "string", "name": "location", "key": 302}, {"content": "", "type": "string", "name": "mime-type", "key": 302}, {"content": 598, "type": "string", "name": "size", "key": 302}, {"content": "Sun Aug 25 07:29:52 2013", "type": "string", "name": "created_date", "key": 302}, {"content": "Mon May 30 15:29:18 2011", "type": "string", "name": "modified_date", "key": 302}, {"content": "version.ini", "type": "string", "name": "name", "key": 303}, {"content": "D:/Java/crawler projects/.metadata/version.ini", "type": "string", "name": "location", "key": 303}, {"content": "ini", "type": "string", "name": "mime-type", "key": 303}, {"content": 26, "type": "string", "name": "size", "key": 303}, {"content": "Sun Aug 25 07:29:52 2013", "type": "string", "name": "created_date", "key": 303}, {"content": "Mon May 30 15:29:18 2011", "type": "string", "name": "modified_date", "key": 303}, {"content": ".mylyn", "type": "string", "name": "name", "key": 4}, {"content": "D:/Java/crawler projects/.metadata/.mylyn", "type": "string", "name": "location", "key": 4}, {"content": "File folder", "type": "string", "name": "type", "key": 4}, {"content": 920, "type": "int", "name": "size", "key": 4}, {"content": 1, "type": "string", "name": "child_folder_count", "key": 4}, {"content": 1, "type": "string", "name": "child_file_count", "key": 4}, {"content": ".tasks.xml.zip", "type": "string", "name": "name", "key": 401}, {"content": "D:/Java/crawler projects/.metadata/.mylyn/.tasks.xml.zip", "type": "string", "name": "location", "key": 401}, {"content": "zip", "type": "string", "name": "mime-type", "key": 401}, {"content": 250, "type": "string", "name": "size", "key": 401}, {"content": "Sun Aug 25 07:29:52 2013", "type": "string", "name": "created_date", "key": 401}, {"content": "Mon May 30 12:23:18 2011", "type": "string", "name": "modified_date", "key": 401}, {"content": "repositories.xml.zip", "type": "string", "name": "name", "key": 402}, {"content": "D:/Java/crawler projects/.metadata/.mylyn/repositories.xml.zip", "type": "string", "name": "location", "key": 402}, {"content": "zip", "type": "string", "name": "mime-type", "key": 402}, {"content": 420, "type": "string", "name": "size", "key": 402}, {"content": "Sun Aug 25 07:29:52 2013", "type": "string", "name": "created_date", "key": 402}, {"content": "Mon May 30 12:23:18 2011", "type": "string", "name": "modified_date", "key": 402}, {"content": "tasks.xml.zip", "type": "string", "name": "name", "key": 403}, {"content": "D:/Java/crawler projects/.metadata/.mylyn/tasks.xml.zip", "type": "string", "name": "location", "key": 403}, {"content": "zip", "type": "string", "name": "mime-type", "key": 403}, {"content": 250, "type": "string", "name": "size", "key": 403}, {"content": "Sun Aug 25 07:29:52 2013", "type": "string", "name": "created_date", "key": 403}, {"content": "Mon May 30 15:31:16 2011", "type": "string", "name": "modified_date", "key": 403}, {"content": "contexts", "type": "string", "name": "name", "key": 5}, {"content": "D:/Java/crawler projects/.metadata/.mylyn/contexts", "type": "string", "name": "location", "key": 5}, {"content": "File folder", "type": "string", "name": "type", "key": 5}, {"content": 0, "type": "int", "name": "size", "key": 5}, {"content": 0, "type": "string", "name": "child_folder_count", "key": 5}]

As i am adding the json documents approx 100 of the json document of approx 15 MB each, or adding more n more filter conditions. The query take more than 1 minute of time, and some time the Browser is not responding.

I am doing this experiment on Intel core i3 2.4 GHz, 4 GB RAM, and 160GB SATA Hard drive.

Kindly tell me, First, how to improve the performance of the query? Whether i need to change my storage structure or change the syntax of my query. and how to perform the join operations on the multiple documents which has the same key, For example, "retrieve the name of document of type xml".

Answer 1

There should be a few ways to improve this query's performance:

• selecting all documents from collection DSP via a subquery and then iterating over them ( for k in (for t in DSP return [t.data]) for z in k for p in z filter p.name == "name" ... ) may be less efficient than using the documents directly. Try replacing the 4 FOR loops and the subquery with just FOR k IN DSP FOR p IN k.data FILTER p.name == "name" ... )

• if you look at the query's explain output it will show that no index will be used. If you have lots of documents in the collection and only want to retrieve a few of them with a query, an index will help performance-wise. I suggest using an array index on data[*].name and one on data[*].content . You can set them up like this: db.DSP.ensureIndex({ type: "hash", fields: [ "data[*].name" ] }); db.DSP.ensureIndex({ type: "hash", fields: [ "data[*].content" ] }); db.DSP.ensureIndex({ type: "hash", fields: [ "data[*].name" ] }); db.DSP.ensureIndex({ type: "hash", fields: [ "data[*].content" ] }); . Note: these types of indexes require ArangoDB 2.8. With these indexes, the query can also be simplified to: FOR p in DSP FILTER "name" IN p.data[*].name || "Book" IN p.data[*].name || "pdf" IN p.data[*].content... FOR p in DSP FILTER "name" IN p.data[*].name || "Book" IN p.data[*].name || "pdf" IN p.data[*].content... FOR p in DSP FILTER "name" IN p.data[*].name || "Book" IN p.data[*].name || "pdf" IN p.data[*].content... . Note that indexes will only help you to quickly find the documents containing the search data, but not the parts of the document that contain it.

• it may be helpful to adjust the document structure. Your current structure seems to contain multiple content and name values per document, eg [ {"content": "Java", "type": "string", "name": "name", "key": 1}, {"content": "D:/Java", "type": "string", "name": "location", "key": 1} ] . It looks like each document has only a data property which is an array these structures. Instead of using this structure, you may try saving each array value as a separate document. For example, {"content": "Java", "type": "string", "name": "name", "key": 1} would become a document of its own, {"content": "D:/Java", "type": "string", "name": "location", "key": 1} would become another document etc. This seems sensible as your sub-structures seem to have a key attribute already and several array values seem to refer to the same key value. The transformation will allow splitting the potentially very big documents into much smaller chunks, and this will not only make the AQL run quicker (as it will need to unpack far less data when accessing a document), but will also allow you to get rid of all the nested loops and locating to the relevant inner array values when returning the result.

Should you adjust the document structure, your query can then be greatly simplified to just FOR p IN DSP FILTER "name" IN p.data[*].name || "Book" IN p.data[*].name || "pdf" IN p.data[*].content ... RETURN p FOR p IN DSP FILTER "name" IN p.data[*].name || "Book" IN p.data[*].name || "pdf" IN p.data[*].content ... RETURN p FOR p IN DSP FILTER "name" IN p.data[*].name || "Book" IN p.data[*].name || "pdf" IN p.data[*].content ... RETURN p and should be fast if indexes are used.