Setting up the data #
Ingesting from CSV using pyarrow #
The data we’ll be using is the Github events data, specifically the Citus events.csv version that’s smaller.
Let’s insert the events data into the databases, I’ll be using pyarrow’s CSV module:
import duckdb
import pyarrow as pa
import pyarrow.csv as csv
conn = duckdb.connect("events.db")
opts = csv.ReadOptions(
column_names=[
"event_id",
"event_type",
"event_public",
"repo_id",
"payload",
"repo",
"user_id",
"org",
"created_at",
]
)
events_csv = csv.read_csv("datasets/gh-events.csv", read_options=opts)
conn.sql("create table events as select * from events_csv")
Here’s the inferred schema we get:
describe events;
┌──────────────┬─────────────┬─────────┬─────────┬─────────┬───────┐
│ column_name │ column_type │ null │ key │ default │ extra │
│ varchar │ varchar │ varchar │ varchar │ varchar │ int32 │
├──────────────┼─────────────┼─────────┼─────────┼─────────┼───────┤
│ event_id │ BIGINT │ YES │ │ │ │
│ event_type │ VARCHAR │ YES │ │ │ │
│ event_public │ VARCHAR │ YES │ │ │ │
│ repo_id │ BIGINT │ YES │ │ │ │
│ payload │ VARCHAR │ YES │ │ │ │
│ repo │ VARCHAR │ YES │ │ │ │
│ user_id │ BIGINT │ YES │ │ │ │
│ org │ VARCHAR │ YES │ │ │ │
│ created_at │ TIMESTAMP_S │ YES │ │ │ │
└──────────────┴─────────────┴─────────┴─────────┴─────────┴───────┘
Alternative: DuckDB’s CSV ingestion #
We could also have used DuckDB’s read_csv_auto. I used pyarrow purely out of
habit. For CSV, DuckDB has its advantages over pyarrow. For example, it’s able
to detect that event_public is boolean and empty strings in org are null.
create table events as
select * from read_csv_auto('datasets/gh-events.csv');
Handling low-cardinality string columns #
event_type has low-cardinality, no point leaving it as VARCHAR:
select count(distinct event_type) from events;
-- 14
Let’s convert it into an enum, counting on DuckDB’s feature for automatically casting enums to varchar whenever necessary.
begin;
create type EVENT_TYPE as enum(
select distinct event_type from events);
alter table events
alter column event_type
set data type EVENT_TYPE;
commit;
Boolean values #
event_public column should be boolean instead of varchar, since it consists of
’t’ and ‘f’ values:
alter table events
alter event_public set data type BOOLEAN;
No need to wrap this within a transaction since all the necessary changes can be done within a single statement.
Checking that JSON is valid #
DuckDB provides the json_valid to check if values are valid json. Given that
org, repo and payload should be json columns, let’s check how many values
are in fact valid json:
pivot (
select
'repo_invalid' as col,
count(*) as c
from events
where json_valid(repo) <> True
union all
select
'payload_invalid' as col,
count(*) as c
from events
where json_valid(payload) <> True
union all
select
'org_invalid' as col,
count(*) as c
from events
where json_valid(org) <> True
)
on col using first(c)
This results in:
┌─────────────┬─────────────────┬──────────────┐
│ org_invalid │ payload_invalid │ repo_invalid │
│ int64 │ int64 │ int64 │
├─────────────┼─────────────────┼──────────────┤
│ 89272 │ 0 │ 0 │
└─────────────┴─────────────────┴──────────────┘
It seems org has a couple of non-json values. Let’s get a sample:
select org
from events
where json_valid(org) <> True
using sample 5 rows;
This gives:
┌─────────┐
│ org │
│ varchar │
├─────────┤
│ │
│ │
│ │
│ │
└─────────┘
Either there’s a lot of empty strings, whitespace or newlines I’m not quite sure.
Let’s get rid of the whitespace (replace with empty strings):
update events
set org = trim(org)
where json_valid(org) <> True;
Next, let’s set all the empty strings to NULL:
update events
set org=NULL
where org = '';
select count(*) from events where org is NULL; -- 89272
JSON data type #
DuckDB provides a JSON logical type. Given that org, repo and payload are
VARCHAR, converting them to JSON does not alter them as per the docs, instead
they are only parsed and validated.
Still, let’s convert them to JSON, it doesn’t hurt.
Ideally, I’d love to use this alter statement, but as of v0.8.1 it does not
work:
alter table events
alter column repo set data type JSON using json(repo);
So we have to use this workaround:
begin;
alter table events add column temp JSON;
update events set temp = json(repo);
alter table events drop repo;
alter table events rename temp to repo;
commit;
Rinse and repeat for the payload and org columns.
Deriving structure from JSON #
DuckDB provides the json_structure function to get the structure of a JSON
value.
Let’s start with org. I’ve placed the query in file ‘query.sql’ and piped the
output to jq so that it’ll pretty-print the json. The query is as follows:
copy (
select distinct
json_structure(org) as structure
from events
where org is not null
) to '/dev/stdout'
with (format 'json')
And the shell command:
duckdb -json events.db < query.sql | jq .structure
For org, this outputs:
{
"id": "UBIGINT",
"url": "VARCHAR",
"login": "VARCHAR",
"avatar_url": "VARCHAR",
"gravatar_id": "VARCHAR"
}
Doing the same for repo we get:
{
"id": "UBIGINT",
"url": "VARCHAR",
"name": "VARCHAR"
}
However, payload isn’t as straightforward, we’ve got 415 different schemas:
select count(*)
from (
select distinct
json_structure(payload) as s
from events
);
-- 415
We’ll have to handle it differently.
Converting JSON to struct #
Before going any further, since repo and org have a given structure, let’s
convert them to struct values.
This is not just for the sake of it, structs have a performance and space advantage over JSON since internally, the struct’s fields are maintained as separate columns (which is DuckDB’s bread and butter). Also querying struct values is more ergonomic: we use dot operators rather than JSON paths. Still, you should only consider converting JSON to struct if you’re sure the schema is static and it’s values aren’t too nested.
Recall we used json_structure to retrieve the structure for repo and org.
We now plug the respective results into json_transform_strict to convert the
JSON values into structs:
For repo:
alter table events
alter column repo
set data type struct(id uinteger, url varchar, name varchar)
using json_transform_strict(
repo, '{"id":"UBIGINT","url":"VARCHAR","name":"VARCHAR"}'
);
For org:
alter table events
alter column org
set data type struct(
id uinteger,
url varchar,
login varchar,
avatar_url varchar,
gravatar_id varchar
)
using json_transform_strict(
org,
'{
"id": "UBIGINT",
"url": "VARCHAR",
"login": "VARCHAR",
"avatar_url": "VARCHAR",
"gravatar_id": "VARCHAR"
}'
);
Handling heterogeneous JSON #
Back to payload values. We could use json_group_structure to get the
combined json_structure of all the payload values:
select
json_group_structure(payload) as structure
from events
But this ends up being a really huge structure; piping it to jq then counting
the lines with wc, I get 784 lines. Using json_keys (this returns the keys
of a json object as a list of strings), there are 20 distinct top-level keys:
with t as (
select
json_group_structure(payload) as structure
from events
)
select
count(*)
from (
select unnest(json_keys(structure)) from t
) -- 20
My hunch is that for each event_type we’ve got a different structure for
payload:
Let’s do a rough test:
with t as (
select
event_type,
count(distinct json_structure(payload)) as s_count,
json_group_structure(payload) as structure
from events
group by 1
)
select
event_type,
s_count,
list_sort(json_keys(structure))[:3] as keys -- pick first 3 only
from t
order by s_count desc;
This results in:
┌───────────────────────────────┬─────────┬───────────────────────────────────────────┐
│ event_type │ s_count │ keys │
│ event_type │ int64 │ varchar[] │
├───────────────────────────────┼─────────┼───────────────────────────────────────────┤
│ PullRequestEvent │ 208 │ [action, number, pull_request] │
│ IssueCommentEvent │ 77 │ [action, comment, issue] │
│ PullRequestReviewCommentEvent │ 61 │ [action, comment, pull_request] │
│ IssuesEvent │ 42 │ [action, issue] │
│ ForkEvent │ 8 │ [forkee] │
│ ReleaseEvent │ 6 │ [action, release] │
│ CreateEvent │ 4 │ [description, master_branch, pusher_type] │
│ PushEvent │ 2 │ [before, commits, distinct_size] │
│ CommitCommentEvent │ 2 │ [comment] │
│ GollumEvent │ 1 │ [pages] │
│ WatchEvent │ 1 │ [action] │
│ MemberEvent │ 1 │ [action, member] │
│ DeleteEvent │ 1 │ [pusher_type, ref, ref_type] │
│ PublicEvent │ 1 │ [] │
├───────────────────────────────┴─────────┴───────────────────────────────────────────┤
│ 14 rows 3 columns │
└─────────────────────────────────────────────────────────────────────────────────────┘
Some event types like DeleteEvent and MemberEvent have a single structure while others (PullRequestEvent, IssueCommentEvent etc) have multiple structures. Since the data’s from github, at this point the best step is to find where they’ve documented payload’s structure so as to understand it better.
Querying JSON #
Let’s query the payloads for push events. Using json_group_structure, we get
the following schema for Push events:
{
"ref": "VARCHAR",
"head": "VARCHAR",
"size": "UBIGINT",
"before": "VARCHAR",
"commits": [
{
"sha": "VARCHAR",
"url": "VARCHAR",
"author": {
"name": "VARCHAR",
"email": "VARCHAR"
},
"message": "VARCHAR",
"distinct": "BOOLEAN"
}
],
"push_id": "UBIGINT",
"distinct_size": "UBIGINT"
}
Let’s get the average number of commits per push. For this, we’re using
JSON Pointer to specify the
location of the "commits" field.
select
avg(
json_array_length(payload, '/commits')
) as average_num_commits,
from events
where event_type = 'PushEvent'
The result:
┌─────────────────────┐
│ average_num_commits │
│ double │
├─────────────────────┤
│ 1.5937930592495273 │
└─────────────────────┘
However this doesn’t give us the “true” average since the list of commits in payload holds only up to 20 commits and the rest have to be fetched separately. We have to use the “size” field instead:
select
avg(
cast(json_extract_string(payload,'size') as UINTEGER)
) as average
from events
where event_type = 'PushEvent'
we get:
┌────────────────────┐
│ average │
│ double │
├────────────────────┤
│ 3.0845803826064664 │
└────────────────────┘
So on average, each push has around 3 commits.
Next, each commit has an author. Let’s get the top 5 authors by number of commits made. For this we’ll use JSONPath to pluck out the fields.
with t as (
select
unnest(
from_json(json_extract(payload, '$.commits'),
'["JSON"]')
) as v
from events
where event_type = 'PushEvent'
)
select
v->>'$.author.name' as name,
count(*) as num_commits
from t
group by 1
order by 2 desc
limit 5
This gives:
┌─────────────────────────┬─────────────┐
│ name │ num_commits │
│ varchar │ int64 │
├─────────────────────────┼─────────────┤
│ OpenLocalizationService │ 947 │
│ Costa Tsaousis │ 780 │
│ Jason Calabrese │ 550 │
│ Junio C Hamano │ 395 │
│ Jiang Xin │ 341 │
└─────────────────────────┴─────────────┘
Note that I used ->> to extract the authors’ names since I want it to be
VARCHAR instead of JSON.
Finally, let’s search through commit messages
First let’s unnest the commit messages. We’ll store the commit messages in a
temporary table to simplify querying. Temporary tables are session scoped and
once we exit, they’ll be deleted. We’ll also set temp_directory so that the
table can be spilled to disk if memory is constrained.
create temporary table commit_messages as
select
row_number() over() as id, -- assign sequential id
v->>'sha' as commit_hash,
v->>'message' as msg,
event_id -- to retrieve associated event
from (
select
event_id,
unnest(
from_json(json_extract(payload, '$.commits'),
'["JSON"]')
) as v
from events
where event_type = 'PushEvent'
);
set temp_directory='.';
Let’s start by retrieving the percentage of commit messages that start with ‘merge pull request’:
select
round((
count(*) /
(select count(*) from commit_messages)
) * 100, 2) as percentage
from commit_messages m1,
where starts_with(lower(m1.msg), 'merge pull request')
This gives:
┌────────────┐
│ percentage │
│ double │
├────────────┤
│ 4.47 │
└────────────┘
Next, let’s build a full text search index over the commit messages:
pragma create_fts_index('commit_messages', 'id', 'msg');
We can now search stuff. For example, to get commit messages containing the term “sqlite”:
select
event_id,
msg
from (
select
*,
fts_main_commit_messages.match_bm25(id, 'sqlite') as score
from commit_messages
) t
where score is not null
order by score desc
This results in 31 rows:
┌────────────┬──────────────────────────┐
│ event_id │ msg │
│ int64 │ varchar │
├────────────┼──────────────────────────┤
│ 4950877801 │ Remove sqlite3 │
│ 4951160992 │ added sqlite3 │
│ 4950954420 │ Delete db.sqlite3 │
│ 4951123085 │ Update #11\n\nAdd SQLite │
│ 4951201866 │ add sqlite config │
└────────────┴──────────────────────────┘
Wrapping up #
There’s lots of other queries to try out, especially on the full github dataset. Querying JSON can be tricky since it involves a lot of plucking fields and unnesting entries, plus different values might not have the same schema. DuckDB does make things a bit easier though by providing various functions and helpers. And some SQL know-how really goes a long way when dealing with JSON.