sakthikumar
asked on
Getting invalid LOB locator error while parsing a binary
I wanted to parse a binary value and store it in a blob column in a database.
Input is a JSON format,
I have tried using the below code, I am getting error (Invalid LOB Locator) on the below line
DBMS_LOB.CONVERTTOCLOB ......
Below is my anonymous block used in apex rest service.
Input is a JSON format,
I have tried using the below code, I am getting error (Invalid LOB Locator) on the below line
DBMS_LOB.CONVERTTOCLOB ......
Below is my anonymous block used in apex rest service.
DECLARE
L_PROFILE_X BLOB;
--Can only read body once as BLOB (this package assumes a base 64 encoded string)
v_body_blob_1 BLOB ;
v_body_blob_test BLOB ;
--BLOB for binary image and CLOB for base 64 encoded
v_image_blob_1 BLOB;
v_clob_1 CLOB;
--CLOB Conversion Parameters
l_src_offset_1 NUMBER := 1;
l_dest_offset_1 NUMBER := 1;
l_blob_csid_1 NUMBER := DBMS_LOB.DEFAULT_CSID;
l_lang_context_1 NUMBER := DBMS_LOB.DEFAULT_LANG_CTX;
l_warning_1 NUMBER;
l_amount_1 NUMBER;
--Can only read body once as BLOB (this package assumes a base 64 encoded string)
v_body_blob_2 BLOB ;
--BLOB for binary image and CLOB for base 64 encoded
v_image_blob_2 BLOB;
v_clob_2 CLOB;
--CLOB Conversion Parameters
l_src_offset_2 NUMBER := 1;
l_dest_offset_2 NUMBER := 1;
l_blob_csid_2 NUMBER := DBMS_LOB.DEFAULT_CSID;
l_lang_context_2 NUMBER := DBMS_LOB.DEFAULT_LANG_CTX;
l_warning_2 NUMBER;
l_amount_2 NUMBER;
BEGIN
L_PROFILE_X := :body;
for i in
(SELECT id,hosted_by,episode_guest,link,go_live_description,logo_image,mime_type_logo,bg,mime_type_bg
FROM json_table
(L_PROFILE_X , '$'
COLUMNS
(id NUMBER FORMAT JSON PATH '$.id',
hosted_by NUMBER FORMAT JSON PATH '$.hosted_by',
episode_guest NUMBER FORMAT JSON PATH '$.episode_guest',
link NUMBER FORMAT JSON PATH '$.link',
go_live_description NUMBER PATH '$.go_live_description',
logo_image varchar2 FORMAT JSON PATH '$.logo.logo_image',
mime_type_logo VARCHAR2 FORMAT JSON PATH '$.logo.logo_image',
bg varchar2 FORMAT JSON PATH '$.background.bg',
mime_type_bg VARCHAR2 PATH '$.background.mime_type'
)
)
)
loop
v_body_blob_1 := UTL_RAW.CAST_TO_RAW (i.logo_image);
v_body_blob_2 := UTL_RAW.CAST_TO_RAW (i.bg);
--Create read buffer for converting base 64 blob to clob
DBMS_LOB.CREATETEMPORARY(v_clob_1,true);
--Get length
l_amount_1 := DBMS_LOB.GETLENGTH(v_body_blob_1);
--Conversion to CLOB
DBMS_LOB.CONVERTTOCLOB
(
v_clob_1,
v_body_blob_1,
l_amount_1,
l_src_offset_1,
l_dest_offset_1,
l_blob_csid_1,
l_lang_context_1,
l_warning_1
);
--Decode CLOB to binary BLOB
v_image_blob_1 := APEX_WEB_SERVICE.CLOBBASE642BLOB(v_clob_1);
------------image 2 ----------------
--Create read buffer for converting base 64 blob to clob
DBMS_LOB.CREATETEMPORARY(v_clob_2,true);
--Get length
l_amount_2 := DBMS_LOB.GETLENGTH(v_body_blob_2);
--Conversion to CLOB
DBMS_LOB.CONVERTTOCLOB
(
v_clob_2,
v_body_blob_2,
l_amount_2,
l_src_offset_2,
l_dest_offset_2,
l_blob_csid_2,
l_lang_context_2,
l_warning_2
);
--Decode CLOB to binary BLOB
v_image_blob_2 := APEX_WEB_SERVICE.CLOBBASE642BLOB(v_clob_2);
if i.id is null then
insert into Go_lives (id,hosted_by,episode_guest,link,go_live_description,mime_type_logo,logo,mime_type_bg,background)
values (Go_lives_seq.nextval,i.hosted_by,i.episode_guest,i.link,i.go_live_description,i.mime_type_logo,v_image_blob_1,i.mime_type_bg,v_image_blob_2);
else
update Go_lives
set hosted_by = i.hosted_by,
episode_guest = i.episode_guest,
link =i.link,
go_live_description = i.go_live_description,
mime_type_logo = i.mime_type_logo,
logo = v_image_blob_1,
mime_type_bg = i.mime_type_bg,
background = v_image_blob_2
where id = i.id;
end if;
end loop;
end;
--------------
Below is the code I am using in Post man to post the data.
{
"id":"",
"hosted_by":"test",
"episode_guest":"test",
"link":"test",
"go_live_description":"test",
"logo":[
{
"logo":"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",
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",
"mime_type":"jpg"
}
]
}
ASKER
Hi SDSTUBER,
I tried the code, but I am getting error on varchar2(32767), "specified length too long for its datatype."
Is there any other datatype we can use..
I tried the code, but I am getting error on varchar2(32767), "specified length too long for its datatype."
Is there any other datatype we can use..
ASKER CERTIFIED SOLUTION
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ASKER
This is simply superb.
When you do this...
L_PROFILE_X := :body;
You are treating your json, which is text, as a blob, which is a Binary Large Object.
L_PROFILE_X should be declared as a CLOB, or VARCHAR2 of at least 23K , not as a BLOB.
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Based on the json you are passing in, your json paths are not correct within the json_table function call
logo_image varchar2 FORMAT JSON PATH '$.logo.logo_image',
mime_type_logo VARCHAR2 FORMAT JSON PATH '$.logo.logo_image',
should be
logo_image VARCHAR2(32767) PATH '$.logo.logo',
mime_type_logo VARCHAR2(3) PATH '$.logo.mime_type',
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I don't know why you're trying to do the utl_raw.cast_to_raw and convert to clob steps, not only is it causing the error you're trying to fix, even if it worked the steps are completely unnecessary. Your image values are already text values of type varchar2 which can be passed in to the apex_webservice function
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After fixing all of these, your code block could be simplified to just...
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Or, embedding the json within the block...
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