This document is for a development version of Ceph.

Ceph s3 select


The purpose of the s3 select engine is to create an efficient pipe between user client and storage nodes (the engine should be close as possible to storage).
It enables selection of a restricted subset of (structured) data stored in an S3 object using an SQL-like syntax.
It also enables for higher level analytic-applications (such as SPARK-SQL) , using that feature to improve their latency and throughput.
For example, a s3-object of several GB (CSV file), a user needs to extract a single column which filtered by another column.
As the following query:
select customer-id from s3Object where age>30 and age<65;
Currently the whole s3-object must retrieve from OSD via RGW before filtering and extracting data.
By “pushing down” the query into OSD , it’s possible to save a lot of network and CPU(serialization / deserialization).
The bigger the object, and the more accurate the query, the better the performance.

Basic workflow

S3-select query is sent to RGW via AWS-CLI
It passes the authentication and permission process as an incoming message (POST).
RGWSelectObj_ObjStore_S3::send_response_data is the “entry point”, it handles each fetched chunk according to input object-key.
send_response_data is first handling the input query, it extracts the query and other CLI parameters.
Per each new fetched chunk (~4m), RGW executes s3-select query on it.
The current implementation supports CSV objects and since chunks are randomly “cutting” the CSV rows in the middle, those broken-lines (first or last per chunk) are skipped while processing the query.
Those “broken” lines are stored and later merged with the next broken-line (belong to the next chunk), and finally processed.
Per each processed chunk an output message is formatted according to AWS specification and sent back to the client.
RGW supports the following response: {:event-type,records} {:content-type,application/octet-stream} {:message-type,event}.
For aggregation queries the last chunk should be identified as the end of input, following that the s3-select-engine initiates end-of-process and produces an aggregate result.

Basic functionalities

S3select has a definite set of functionalities that should be implemented (if we wish to stay compliant with AWS), currently only a portion of it is implemented.
The implemented software architecture supports basic arithmetic expressions, logical and compare expressions, including nested function calls and casting operators, that alone enables the user reasonable flexibility.
review the below s3-select-feature-table.

Error Handling

Any error occurs while the input query processing, i.e. parsing phase or execution phase, is returned to client as response error message.
Fatal severity (attached to the exception) will end query execution immediately, other error severity are counted, upon reaching 100, it ends query execution with an error message.

Features Support

Currently only part of AWS select command is implemented, table below describes what is currently supported.
The following table describes the current implementation for s3-select functionalities:



Example / Description

Arithmetic operators

^ * % / + - ( )

select (int(_1)+int(_2))*int(_9) from s3object;

% modulo

select count(*) from s3object where cast(_1 as int)%2 == 0;

^ power-of

select cast(2^10 as int) from s3object;

Compare operators

> < >= <= == !=

select _1,_2 from s3object where (int(_1)+int(_3))>int(_5);

logical operator


select count(*) from s3object where not (int(1)>123 and int(_5)<200);

logical operator

is null

return true/false for null indication in expression

logical operator

is not null

return true/false for null indication in expression

logical operator and NULL

unknown state

review null-handle observe how logical operator result with null. the following query return 0. select count(*) from s3object where null and (3>2);

Arithmetic operator with NULL

unknown state

review null-handle observe the results of binary operations with NULL the following query return 0. select count(*) from s3object where (null+1) and (3>2);

compare with NULL

unknown state

review null-handle observe results of compare operations with NULL the following query return 0. select count(*) from s3object where (null*1.5) != 3;

missing column

unknown state

select count(*) from s3object where _1 is null;

projection column

similar to if/then/else

select case

when (1+1==(2+1)*3) then ‘case_1’ when ((4*3)==(12)) then ‘case_2’ else ‘case_else’ end, age*2 from s3object;

logical operator

coalesce :: return first non-null argumnet

select coalesce(nullif(5,5),nullif(1,1.0),age+12) from s3object;

logical operator

nullif :: return null in case both arguments are equal, or else the first one
nullif(1,1)=NULL nullif(null,1)=NULL nullif(2,1)=2

select nullif(cast(_1 as int),cast(_2 as int)) from s3object;

logical operator

{expression} in ( .. {expression} ..)
select count(*) from s3object

where ‘ben’ in (trim(_5),substring(_1,char_length(_1)-3,3),last_name);

logical operator

{expression} between {expression} and {expression}
select count(*) from stdin
where substring(_3,char_length(_3),1) between “x” and trim(_1)

and substring(_3,char_length(_3)-1,1) == “:”;

logical operator

{expression} like {match-pattern}

select count(*) from s3object where first_name like ‘%de_’; select count(*) from s3object where _1 like "%a[r-s];

casting operator

select cast(123 as int)%2 from s3object;

casting operator

select cast(123.456 as float)%2 from s3object;

casting operator

select cast(‘ABC0-9’ as string),cast(substr(‘ab12cd’,3,2) as int)*4 from s3object;

casting operator

select cast(substring(‘publish on 2007-01-01’,12,10) as timestamp) from s3object;

non AWS casting operator

select int(_1),int( 1.2 + 3.4) from s3object;

non AWS casting operator

select float(1.2) from s3object;

not AWS casting operator

select timestamp(‘1999:10:10-12:23:44’) from s3object;

Aggregation Function


select sum(int(_1)) from s3object;

Aggregation Function


select avg(cast(_1 a float) + cast(_2 as int)) from s3object;

Aggregation Function


select min( int(_1) * int(_5) ) from s3object;

Aggregation Function


select max(float(_1)),min(int(_5)) from s3object;

Aggregation Function


select count(*) from s3object where (int(1)+int(_3))>int(_5);

Timestamp Functions


select count(*) from s3object where extract(‘year’,timestamp(_2)) > 1950 and extract(‘year’,timestamp(_1)) < 1960;

Timestamp Functions


select count(0) from s3object where datediff(‘year’,timestamp(_1),dateadd(‘day’,366,timestamp(_1))) == 1;

Timestamp Functions


select count(0) from s3object where datediff(‘month’,timestamp(_1),timestamp(_2))) == 2;

Timestamp Functions


select count(0) from s3object where datediff(‘hours’,utcnow(),dateadd(‘day’,1,utcnow())) == 24 ;

String Functions


select count(0) from s3object where int(substring(_1,1,4))>1950 and int(substring(_1,1,4))<1960;

String Functions


select trim(’ foobar ‘) from s3object;

String Functions


select trim(trailing from ‘ foobar ‘) from s3object;

String Functions


select trim(leading from ‘ foobar ‘) from s3object;

String Functions


select trim(both ‘12’ from ‘1112211foobar22211122’) from s3objects;

String Functions


select lower(‘ABcD12#$e’) from s3object;

String Functions

char_length character_length

select count(*) from s3object where char_length(_3)==3;

Complex queries

select sum(cast(_1 as int)),

max(cast(_3 as int)), substring(‘abcdefghijklm’, (2-1)*3+sum(cast(_1 as int))/sum(cast(_1 as int))+1, (count() + count(0))/count(0)) from s3object;

alias support

select int(_1) as a1, int(_2) as a2 , (a1+a2) as a3 from s3object where a3>100 and a3<300;


NULL is a legit value in ceph-s3select systems similar to other DB systems, i.e. systems needs to handle the case where a value is NULL.
The definition of NULL in our context, is missing/unknown, in that sense NULL can not produce a value on ANY arithmetic operations ( a + NULL will produce NULL value).
The Same is with arithmetic comaprision, any comparison to NULL is NULL, i.e. unknown.
Below is a truth table contains the NULL use-case.





A OR False


A OR True




A AND False


A AND True


A and A


s3-select function interfaces

Timestamp functions

The timestamp functionalities is partially implemented.
the casting operator( timestamp( string ) ), converts string to timestamp basic type.
Currently it can convert the following pattern yyyy:mm:dd hh:mi:dd
extract( date-part , timestamp) : function return integer according to date-part extract from input timestamp.
supported date-part : year,month,week,day.
dateadd(date-part , integer,timestamp) : function return timestamp, a calculation results of input timestamp and date-part.
supported data-part : year,month,day.
datediff(date-part,timestamp,timestamp) : function return an integer, a calculated result for difference between 2 timestamps according to date-part.
supported date-part : year,month,day,hours.
utcnow() : return timestamp of current time.

Aggregation functions

count() : return integer according to number of rows matching condition(if such exist).
sum(expression) : return a summary of expression per all rows matching condition(if such exist).
avg(expression) : return a average of expression per all rows matching condition(if such exist).
max(expression) : return the maximal result for all expressions matching condition(if such exist).
min(expression) : return the minimal result for all expressions matching condition(if such exist).

String functions

substring(string,from,to) : return a string extract from input string according to from,to inputs.
char_length : return a number of characters in string (character_length does the same).
trim : trims leading/trailing characters from target string, the default is blank character.
upper\lower : converts characters into lowercase/uppercase.


Alias programming-construct is an essential part of s3-select language, it enables much better programming especially with objects containing many columns or in the case of complex queries.
Upon parsing the statement containing alias construct, it replaces alias with reference to correct projection column, on query execution time the reference is evaluated as any other expression.
There is a risk that self(or cyclic) reference may occur causing stack-overflow(endless-loop), for that concern upon evaluating an alias, it is validated for cyclic reference.
Alias also maintains result-cache, meaning upon using the same alias more than once, it’s not evaluating the same expression again(it will return the same result),instead it uses the result from cache.
Of Course, per each new row the cache is invalidated.

Sending Query to RGW

Any http-client can send s3-select request to RGW, it must be compliant with AWS Request syntax.
Sending s3-select request to RGW using AWS cli, should follow AWS command reference.
below is an example for it.
aws --endpoint-url http://localhost:8000 s3api select-object-content
 --bucket {BUCKET-NAME}
 --expression-type 'SQL'
 '{"CSV": {"FieldDelimiter": "," , "QuoteCharacter": "\"" , "RecordDelimiter" : "\n" , "QuoteEscapeCharacter" : "\\" , "FileHeaderInfo": "USE" }, "CompressionType": "NONE"}'
 --output-serialization '{"CSV": {}}'
 --key {OBJECT-NAME}
 --expression "select count(0) from s3object where int(_1)<10;" output.csv


Input serialization (Implemented), it let the user define the CSV definitions; the default values are {\n} for row-delimiter {,} for field delimiter, {”} for quote, {\} for escape characters.
it handle the csv-header-info, the first row in input object containing the schema.
Output serialization is currently not implemented, the same for compression-type.
s3-select engine contain a CSV parser, which parse s3-objects as follows.
- each row ends with row-delimiter.
- field-separator separates between adjacent columns, successive field separator define NULL column.
- quote-character overrides field separator, meaning , field separator become as any character between quotes.
- escape character disables any special characters, except for row delimiter.
Below are examples for CSV parsing rules.

CSV parsing behavior



input ==> tokens


successive field delimiter

,,1,,2, ==> {null}{null}{1}{null}{2}{null}


quote character overrides field delimiter

11,22,”a,b,c,d”,last ==> {11}{22}{“a,b,c,d”}{last}


escape char overrides meta-character. escape removed

11,22,str=\”abcd\”\,str2=\”123\”,last ==> {11}{22}{str=”abcd”,str2=”123”}{last}

row delimiter

no close quote, row delimiter is closing line

11,22,a=”str,44,55,66 ==> {11}{22}{a=”str,44,55,66}

csv header info

FileHeaderInfo tag

USE” value means each token on first line is column-name, “IGNORE” value means to skip the first line


using BOTO3 is “natural” and easy due to AWS-cli support.
def run_s3select(bucket,key,query,column_delim=",",row_delim="\n",quot_char='"',esc_char='\\',csv_header_info="NONE"):
   s3 = boto3.client('s3',

   r = s3.select_object_content(
       InputSerialization = {"CSV": {"RecordDelimiter" : row_delim, "FieldDelimiter" : column_delim,"QuoteEscapeCharacter": esc_char, "QuoteCharacter": quot_char, "FileHeaderInfo": csv_header_info}, "CompressionType": "NONE"},
       OutputSerialization = {"CSV": {}},

   result = ""
   for event in r['Payload']:
       if 'Records' in event:
           records = event['Records']['Payload'].decode('utf-8')
           result += records

   return result

 "select int(_1) as a1, int(_2) as a2 , (a1+a2) as a3 from s3object where a3>100 and a3<300;")