Working with JSON is daily business for Application Express developers. RESTful services typically return JSON, JSON is frequently used to store configuration or other kinds of flexible data, and JSON also becomes more and more a data exchange format. So developers frequently encounter the requirement to parse JSON documents and process data.
Based on the database version, there are multiple alternatives to work with JSON documents in a PL/SQL or SQL context.
When using 12.2 database (or even 18.1), there are a few alternatives to choose from. Many APEX developers just continue using APEX_JSON - because it's known - and probably also because of the APEX_ prefix. But having a closer look into the native JSON functionality is absolutely well-invested time: it can lead to massively improved performance thus lowering general load on the database. This article will show a few comparisons between APEX_JSON and native functionality.
Don't stop reading when you're still on 11.2 or have to support 11.2 databases as well. It might be a good idea to use PL/SQL conditional compilation and add APEX_JSON as well as native PL/SQL JSON parsing to your code. On a more recent database, your application will perform better out-of-the-box. And as soon as you discontinue your 11.2 support, you can simply remove the old code. And the nice side-effect is, that you already trained yourself on the new functionality.
Let's now start with an example to compare the various approaches of JSON parsing in the database. We first need a JSON document to play with - and since the internet is full of them, we simply grab it from there. As for earlier articles and how tos, we'll use Earthquake JSON feeds provided by the US Geological Survey (USGS). With the APEX_WEB_SERVICE package we'll grab them and because we don't want to execute HTTP requests all the time, we'll store them as a CLOB into a table:
create table earthquake_json( id number generated always as identity, fetched_at timestamp default systimestamp, document clob, constraint document_isjson check (document is json) ); insert into earthquake_json( document ) values ( apex_web_service.make_rest_request( p_url => 'https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_month.geojson', p_http_method => 'GET' ) ); select id, fetched_at, dbms_lob.getlength(document) from earthquake_json; ID FETCHED_AT DBMS_LOB.GETLENGTH(DOCUMENT) --- ---------------------------- ---------------------------- 1 26.02.18 02:29:59,988083000 6743973
Depending on the URL being invoked, the JSON contains earthquakes for a day, a week or a month. The JSON document structure is outlined in the screen shot below. The features attribute is an array - each element contains data for one earthquake.
For the below examples, we invoked the URL returning earthquakes for a month. That leads to somewhat larger JSON documents; which is intentional. On a larger scale, we'll clearly see how the different JSON parsers will perform.
First, we want to know, how many array elements are present in the features attribute. First attempt is with APEX_JSON:
declare l_clob clob; l_feature_count pls_integer; l_time timestamp; begin select document into l_clob from earthquake_json where id = 1; l_time := systimestamp; apex_json.parse( p_source => l_clob ); dbms_output.put_line( 'Parsing Time: ' || extract( second from ( systimestamp - l_time ) ) ); l_time := systimestamp; l_feature_count := apex_json.get_count( 'features' ); dbms_output.put_line( 'Array Count: ' || l_feature_count ); dbms_output.put_line( 'Get Array Count Time: ' || extract( second from ( systimestamp - l_time ) ) ); end; Parsing Time: 12,293681 Array Count: 9456 Get Array Count Time: 0,000039
As the output indicates, the call to APEX_JSON.PARSE needed about 12 seconds; that is the time APEX_JSON needed to actually parse the JSON document. Counting the elements of the features array is rather quick; it needed only a very small fraction of a second. During the PARSE call, APEX_JSON built an internal representation of the JSON document; that required all the time. Retrieving information from that internal memory structure is actually pretty cheap. So, when working with APEX_JSON, try to call PARSE only once and to reuse the parsed JSON as often as possible.
Let's now have a look into the PL/SQL Object Types for JSON parsing, introduced in Oracle Database 12.2. These types are JSON_OBJECT_T, JSON_ARRAY_T, JSON_ELEMENT_T, JSON_SCALAR_T and JSON_KEY_LIST. JSON_ELEMENT_T is a generalization of JSON_OBJECT_T, JSON_ARRAY_T or JSON_SCALAR_T, representing either a JSON object, an array or a scalar attribute value. JSON_ELEMENT_T, JSON_OBJECT_T or JSON_ARRAY_T contain a static PARSE method in order to parse a JSON document. When we change the above PL/SQL block in order to use the new PL/SQL object types, the result doesn't look so different ...
declare l_clob clob; l_feature_count pls_integer; l_time timestamp; l_json json_object_t; l_features json_array_t; begin select document into l_clob from earthquake_json where id = 1; l_time := systimestamp; l_json := json_object_t.parse( l_clob ); dbms_output.put_line( 'Parsing Time: ' || extract( second from ( systimestamp - l_time ) ) ); l_time := systimestamp; l_features := l_json.get_array( 'features' ); dbms_output.put_line( 'Array Count: ' || l_features.get_size ); dbms_output.put_line( 'Get Array Count Time: ' || extract( second from ( systimestamp - l_time ) ) ); end; Parsing Time: 0,124148 Array Count: 9456 Get Array Count Time: 0,000083
... but there is a huge difference regarding the consumed time. 0.12 seconds vs 12 seconds - that is about 100 times. For smaller documents, the performance difference is smaller, but it's always there. So JSON parsing with APEX_JSON is much more expensive than with the native functions - which is obvious, since APEX_JSON is a PL/SQL implementation whereas the PL/SQL object types have a C implementation as part of the database itself. And such a thing like JSON parsing (which is mostly string operations) will gain huge benefits from a native implementation.
Now we want to look into the earthquake data in more detail; we want to get some information about the weakest and the strongest earthquake contained in the JSON document. We could do this the same way as we did above: implementing a procedure using APEX_JSON (or better: JSON_OBJECT_T), parse the JSON, then retrieve the data of interest using PL/SQL loops and calling the various APEX_JSON getter functions or JSON_OBJECT_T methods.
But we're in a database, aren't we? The most powerful way to deal with data is to use SQL - and to use SQL functionality with JSON data, we need to treat a JSON document as it was a table. The JSON_TABLE SQL function will come in very handy now ...
with eqdata as ( select e.id, e.title, e.mag from earthquake_json j, json_table( document, '$.features[*]' columns( id varchar2(20) path '$.id', mag number path '$.properties.mag', title varchar2(200) path '$.properties.title' ) ) e ), minmax as ( select min(e.mag) minmag, max(e.mag) maxmag from eqdata e ) select e.id, e.title, e.mag from eqdata e, minmax m where e.mag in ( m.minmag, m.maxmag ) / ID TITLE MAG ----------- ---------------------------------------------- ----- us2000d7q6 M 7.5 - 89km SSW of Porgera, Papua New Guinea 7.5 uw61366531 M -0.8 - 36km NNE of Amboy, Washington -0.8 2 rows selected. Elapsed: 00:00:01.545
JSON_TABLE allows to project attributes of an array within a JSON document as a table - with rows and columns. That table can then be used like a normal table - so within a SQL query we can build subqueries, apply aggregations or all other SQL functionality to the data.
Isn't that an elegant way to parse JSON? The usage of subquery factoring and SQL aggregate functions leads to a clear, structured and very maintainable SQL query. Even more: if, at some day, the data will be present as a normal relational table, it will be super-easy to adopt to this. Most of the query wouldn't even have to be changed. JSON_TABLE is available in 22.214.171.124 or later.
When you're still on 11.2, there's no JSON_TABLE. But does that mean, you cannot use this elegant SQL-centric JSON parsing ...?
No, APEX_JSON will help. It provides the TO_XMLTYPE function which converts a JSON to an XML document. And for XML documents, the SQL function XMLTABLE is available since Oracle 9.2. So, combining APEX_JSON and XMLTABLE will give you the same power as JSON_TABLE does. Here's the SQL query doing the same as above - but that one will run on 11.2 as well.
with eqdata as ( select e.id, e.title, e.mag from earthquake_json j, xmltable( '/json/features/row' passing apex_json.to_xmltype( j.document ) columns id varchar2(20) path 'id/text()', mag number path 'properties/mag/text()', title varchar2(200) path 'properties/title/text()' ) e ), minmax as ( select min(e.mag) minmag, max(e.mag) maxmag from eqdata e ) select e.id, e.title, e.mag from eqdata e, minmax m where e.mag in ( m.minmag, m.maxmag ) / ID TITLE MAG ----------- ---------------------------------------------- ----- us2000d7q6 M 7.5 - 89km SSW of Porgera, Papua New Guinea 7.5 uw61366531 M -0.8 - 36km NNE of Amboy, Washington -0.8 2 rows selected. Elapsed: 00:00:27.152
The results are the same. As we can see in the timing - there is no free lunch: We got the power of SQL to work on the JSON document in 11.2. But we first had to convert to XML with APEX_JSON; and that comes at a cost. 27 seconds is way more than the 1.4 seconds JSON_TABLE consumed by JSON_TABLE. A significant part of this was spent for the JSON to XML conversion ...
select apex_json.to_xmltype(document) from earthquake_json where id = 1; APEX_JSON.TO_XMLTYPE(DOCUMENT) ------------------------------ <json>... Elapsed: 00:00:12.711
... which pretty nicely matches the time consumption of APEX_JSON.PARSE in the first example.
From the results of these simple tests, we can derive some rules for working with JSON in APEX (and also outside of APEX) ...
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