Re: Beam SQL found limitations
It would be interesting to see a design for this. You'll need to partition
or it won't scale because SQL "OVER" clause is linear and sorted in this
case. Other than that, it should be a pretty straightforward implementation
using state + timers + @RequiresTimeSortedInput. Sorting in any other way
would be a little more work, so I'd start with rejecting ORDER BY clauses
with other columns.
Kenn
On Fri, Jun 9, 2023 at 5:06 AM Wiśniowski Piotr <
contact.wisniowskipi...@gmail.com> wrote:
> Hi,
>
> BTW just found this on Calcite:
> https://calcite.apache.org/docs/stream.html#sliding-windows
>
> I think this is precisely what I was trying to do with Beam SQL and the
> syntax is also very intuitive.
>
> Could this be added to SQL roadmap? How hard it is for implementation?
>
> Best
>
> Piotr
> On 31.05.2023 20:29, Kenneth Knowles wrote:
>
> 1. Yes, using state is a better fit than Beam windowing. You will want to
> use the new feature of annotating the DoFn with @RequiresTimeSortedInput.
> This will make it so you can be sure you are actually getting the
> "previous" event. They can arrive in any order without this annotation. You
> won't be able to do this in SQL. I don't think Beam SQL has implementations
> of analytic functions that have this ability.
>
> Kenn
>
> On Wed, May 31, 2023 at 4:17 AM Wiśniowski Piotr <
> contact.wisniowskipi...@gmail.com> wrote:
>
>> Hi Kenn,
>>
>> Thanks for clarification.
>>
>> 1. Just to put an example in front - for every event that comes in I need
>> to find corresponding previous event of same user_id and pass
>> previous_event_timestamp in the current event payload down (and also
>> current event becomes previous event for future events that come in for
>> same user). Question is how to do it with BeamSQL. I am aware that analytic
>> windowing (like last_value over etc.) might not be a way for streaming and
>> I am ok with this - it make sense under the hood just as You mention.
>>
>> The task is to be able to keep a simple state in streaming SQL. What I
>> did come up with is using sliding window to have this state available for
>> each new event that comes in.
>>
>> ```
>>
>> WITH
>> unbounded_stream_initialized AS (
>> SELECT
>> user_id,
>> event_time
>> FROM unbounded_stream
>> GROUP BY
>> user_id,
>> event_time,
>> TUMBLE(event_time,INTERVAL '1' SECONDS)
>> UNION ALL
>> -- this is needed as first session window by default starts at first
>> element, while here we need to start it in the past
>> -- so that there is a window that ends just after first real element
>> SELECT
>> CAST(0 AS BIGINT) AS user_id,
>> CAST('2023-05-19 08:00:00' AS TIMESTAMP) AS event_time
>> FROM UNNEST(ARRAY[0]) AS arr -- this is dummy as syntax does not
>> allow to have GROUP BY just after SELECT
>> GROUP BY TUMBLE(CAST('2023-05-19 08:00:00' AS TIMESTAMP), INTERVAL
>> '1' SECONDS)
>> ),
>> test_data_1 AS (
>> SELECT
>> user_id,
>> MAX(event_time) AS prev_event_time,
>> HOP_END(event_time, INTERVAL '1' SECONDS, INTERVAL '7' DAYS) AS
>> window_end_at
>> FROM unbounded_stream_initialized
>> GROUP BY
>> user_id,
>> HOP(
>> -- first create a sliding window to aggregate state
>> event_time,
>> INTERVAL '1' SECONDS,
>> INTERVAL '7' DAYS -- The idea is to have this quite long
>> compared to interval
>> )
>> ),
>> test_data_1_lookup AS (
>> SELECT
>> user_id,
>> prev_event_time
>> FROM test_data_1
>> GROUP BY
>> user_id,
>> -- then re-window into windows suitable for joining main stream
>> TUMBLE(window_end_at, INTERVAL '1' SECONDS)
>> ),
>> enriched_info AS (
>> SELECT
>> unbounded_stream_initialized.event_timestamp AS event_timestamp,
>> unbounded_stream_initialized.user_id AS user_id,
>> test_data_1_lookup.prev_event_time AS prev_event_time
>> FROM unbounded_stream_initialized
>> LEFT JOIN test_data_1_lookup
>> ON unbounded_stream_initialized.user_id =
>> test_data_1_lookup.user_id
>> )
>> SELECT
>> *
>> FROM enriched_info
>>
>> ```
>>
>> The doubt that I have is whether above will not store too much redundant
>> data as `test_data_1` suggests it could duplicate and store each incoming
>> msg into all windows there are in the sliding window definition (might be a
>> lot in this case). Or actually resolving if a message belongs to a window
>> is done later when evaluating `LEFT JOIN`? Target DataFlow. I am still
>> learning Beam so there might be some core thing that I miss to understand
>> how it is processed.
>>
>> 2. Any hints on implementing FirestoreIOTableProvider? just more or less
>> how to do it where to look for important parts etc. It seems we would need
>> this functionality.
>>
>> 3. I will try to report some more interesting findings. If possible
>> please prioritize fixing this ROW error.
Re: Beam SQL found limitations
1. Yes, using state is a better fit than Beam windowing. You will want to
use the new feature of annotating the DoFn with @RequiresTimeSortedInput.
This will make it so you can be sure you are actually getting the
"previous" event. They can arrive in any order without this annotation. You
won't be able to do this in SQL. I don't think Beam SQL has implementations
of analytic functions that have this ability.
Kenn
On Wed, May 31, 2023 at 4:17 AM Wiśniowski Piotr <
contact.wisniowskipi...@gmail.com> wrote:
> Hi Kenn,
>
> Thanks for clarification.
>
> 1. Just to put an example in front - for every event that comes in I need
> to find corresponding previous event of same user_id and pass
> previous_event_timestamp in the current event payload down (and also
> current event becomes previous event for future events that come in for
> same user). Question is how to do it with BeamSQL. I am aware that analytic
> windowing (like last_value over etc.) might not be a way for streaming and
> I am ok with this - it make sense under the hood just as You mention.
>
> The task is to be able to keep a simple state in streaming SQL. What I did
> come up with is using sliding window to have this state available for each
> new event that comes in.
>
> ```
>
> WITH
> unbounded_stream_initialized AS (
> SELECT
> user_id,
> event_time
> FROM unbounded_stream
> GROUP BY
> user_id,
> event_time,
> TUMBLE(event_time,INTERVAL '1' SECONDS)
> UNION ALL
> -- this is needed as first session window by default starts at first
> element, while here we need to start it in the past
> -- so that there is a window that ends just after first real element
> SELECT
> CAST(0 AS BIGINT) AS user_id,
> CAST('2023-05-19 08:00:00' AS TIMESTAMP) AS event_time
> FROM UNNEST(ARRAY[0]) AS arr -- this is dummy as syntax does not allow
> to have GROUP BY just after SELECT
> GROUP BY TUMBLE(CAST('2023-05-19 08:00:00' AS TIMESTAMP), INTERVAL '1'
> SECONDS)
> ),
> test_data_1 AS (
> SELECT
> user_id,
> MAX(event_time) AS prev_event_time,
> HOP_END(event_time, INTERVAL '1' SECONDS, INTERVAL '7' DAYS) AS
> window_end_at
> FROM unbounded_stream_initialized
> GROUP BY
> user_id,
> HOP(
> -- first create a sliding window to aggregate state
> event_time,
> INTERVAL '1' SECONDS,
> INTERVAL '7' DAYS -- The idea is to have this quite long
> compared to interval
> )
> ),
> test_data_1_lookup AS (
> SELECT
> user_id,
> prev_event_time
> FROM test_data_1
> GROUP BY
> user_id,
> -- then re-window into windows suitable for joining main stream
> TUMBLE(window_end_at, INTERVAL '1' SECONDS)
> ),
> enriched_info AS (
> SELECT
> unbounded_stream_initialized.event_timestamp AS event_timestamp,
> unbounded_stream_initialized.user_id AS user_id,
> test_data_1_lookup.prev_event_time AS prev_event_time
> FROM unbounded_stream_initialized
> LEFT JOIN test_data_1_lookup
> ON unbounded_stream_initialized.user_id =
> test_data_1_lookup.user_id
> )
> SELECT
> *
> FROM enriched_info
>
> ```
>
> The doubt that I have is whether above will not store too much redundant
> data as `test_data_1` suggests it could duplicate and store each incoming
> msg into all windows there are in the sliding window definition (might be a
> lot in this case). Or actually resolving if a message belongs to a window
> is done later when evaluating `LEFT JOIN`? Target DataFlow. I am still
> learning Beam so there might be some core thing that I miss to understand
> how it is processed.
>
> 2. Any hints on implementing FirestoreIOTableProvider? just more or less
> how to do it where to look for important parts etc. It seems we would need
> this functionality.
>
> 3. I will try to report some more interesting findings. If possible please
> prioritize fixing this ROW error.
>
> Best
>
> Piotr
>
> On 26.05.2023 21:36, Kenneth Knowles wrote:
>
> Just want to clarify that Beam's concept of windowing is really an
> event-time based key, and they are all processed logically simultaneously.
> SQL's concept of windowing function is to sort rows and process them
> linearly. They are actually totally different. From your queries it seems
> you are interested in SQL's windowing functions (aka analytic functions).
>
> I am surprised by the problems with rows, since we have used them
> extensively. Hopefully it is not too hard to fix. Same with the UNION ALL
> problem.
>
> And for the CROSS JOIN it would be a nice feature to allow in some cases
> it seems. Should not be hard.
>
> Thank you for reporting this! If you have time it would be really great to
> get each of these reproducible problems into GitHub issues, each.
>
> Kenn
>
> On Fri, May 26, 2023 at 11:06 AM Wiśniowski Piotr <
> contact.wisniowskipi...@gmail.com> wrote:
>
>>
Re: Beam SQL found limitations
Hi Kenn,
Thanks for clarification.
1. Just to put an example in front - for every event that comes in I
need to find corresponding previous event of same user_id and pass
previous_event_timestamp in the current event payload down (and also
current event becomes previous event for future events that come in for
same user). Question is how to do it with BeamSQL. I am aware that
analytic windowing (like last_value over etc.) might not be a way for
streaming and I am ok with this - it make sense under the hood just as
You mention.
The task is to be able to keep a simple state in streaming SQL. What I
did come up with is using sliding window to have this state available
for each new event that comes in.
```
WITH
unbounded_stream_initialized AS (
SELECT
user_id,
event_time
FROM unbounded_stream
GROUP BY
user_id,
event_time,
TUMBLE(event_time,INTERVAL '1' SECONDS)
UNION ALL
-- this is needed as first session window by default starts at
first element, while here we need to start it in the past
-- so that there is a window that ends just after first real element
SELECT
CAST(0 AS BIGINT) AS user_id,
CAST('2023-05-19 08:00:00' AS TIMESTAMP) AS event_time
FROM UNNEST(ARRAY[0]) AS arr -- this is dummy as syntax does not
allow to have GROUP BY just after SELECT
GROUP BY TUMBLE(CAST('2023-05-19 08:00:00' AS TIMESTAMP), INTERVAL
'1' SECONDS)
),
test_data_1 AS (
SELECT
user_id,
MAX(event_time) AS prev_event_time,
HOP_END(event_time, INTERVAL '1' SECONDS, INTERVAL '7' DAYS) AS
window_end_at
FROM unbounded_stream_initialized
GROUP BY
user_id,
HOP(
-- first create a sliding window to aggregate state
event_time,
INTERVAL '1' SECONDS,
INTERVAL '7' DAYS -- The idea is to have this quite long
compared to interval
)
),
test_data_1_lookup AS (
SELECT
user_id,
prev_event_time
FROM test_data_1
GROUP BY
user_id,
-- then re-window into windows suitable for joining main stream
TUMBLE(window_end_at, INTERVAL '1' SECONDS)
),
enriched_info AS (
SELECT
unbounded_stream_initialized.event_timestamp AS event_timestamp,
unbounded_stream_initialized.user_id AS user_id,
test_data_1_lookup.prev_event_time AS prev_event_time
FROM unbounded_stream_initialized
LEFT JOIN test_data_1_lookup
ON unbounded_stream_initialized.user_id =
test_data_1_lookup.user_id
)
SELECT
*
FROM enriched_info
```
The doubt that I have is whether above will not store too much redundant
data as `test_data_1` suggests it could duplicate and store each
incoming msg into all windows there are in the sliding window definition
(might be a lot in this case). Or actually resolving if a message
belongs to a window is done later when evaluating `LEFT JOIN`? Target
DataFlow. I am still learning Beam so there might be some core thing
that I miss to understand how it is processed.
2. Any hints on implementing FirestoreIOTableProvider? just more or less
how to do it where to look for important parts etc. It seems we would
need this functionality.
3. I will try to report some more interesting findings. If possible
please prioritize fixing this ROW error.
Best
Piotr
On 26.05.2023 21:36, Kenneth Knowles wrote:
Just want to clarify that Beam's concept of windowing is really an
event-time based key, and they are all processed logically
simultaneously. SQL's concept of windowing function is to sort rows
and process them linearly. They are actually totally different. From
your queries it seems you are interested in SQL's windowing functions
(aka analytic functions).
I am surprised by the problems with rows, since we have used them
extensively. Hopefully it is not too hard to fix. Same with the UNION
ALL problem.
And for the CROSS JOIN it would be a nice feature to allow in some
cases it seems. Should not be hard.
Thank you for reporting this! If you have time it would be really
great to get each of these reproducible problems into GitHub issues, each.
Kenn
On Fri, May 26, 2023 at 11:06 AM Wiśniowski Piotr
wrote:
Hi Alexey,
Thank You for reference to that discussion I do actually have
pretty similar thoughts on what Beam SQL needs.
Update from my side:
Actually did find a workaround for issue with windowing function
on stream. It basically boils down to using sliding window to
collect and aggregate the state. But would need an advice if this
is actually a cost efficient method (targeting DataFlow runner).
The doubt that I have is that this sliding window would need to
have sliding interval less than 1s and size more than a week and
be feed with quire frequent data. If I do understand this
correctly - it would mean each input row would need to be
duplicated for each window and
Re: Beam SQL found limitations
Just want to clarify that Beam's concept of windowing is really an event-time based key, and they are all processed logically simultaneously. SQL's concept of windowing function is to sort rows and process them linearly. They are actually totally different. From your queries it seems you are interested in SQL's windowing functions (aka analytic functions). I am surprised by the problems with rows, since we have used them extensively. Hopefully it is not too hard to fix. Same with the UNION ALL problem. And for the CROSS JOIN it would be a nice feature to allow in some cases it seems. Should not be hard. Thank you for reporting this! If you have time it would be really great to get each of these reproducible problems into GitHub issues, each. Kenn On Fri, May 26, 2023 at 11:06 AM Wiśniowski Piotr < contact.wisniowskipi...@gmail.com> wrote: > Hi Alexey, > > Thank You for reference to that discussion I do actually have pretty > similar thoughts on what Beam SQL needs. > > Update from my side: > > Actually did find a workaround for issue with windowing function on > stream. It basically boils down to using sliding window to collect and > aggregate the state. But would need an advice if this is actually a cost > efficient method (targeting DataFlow runner). The doubt that I have is that > this sliding window would need to have sliding interval less than 1s and > size more than a week and be feed with quire frequent data. If I do > understand this correctly - it would mean each input row would need to be > duplicated for each window and stored which could be quite significant > storage cost? > > Or actually Beam does not physically duplicate the record but just tracks > to which windows the record currently belongs? > > > And the real issue that BeamSQL needs at the moment in my opinion is > fixing bugs. > > Some bugs that I found that prevent one from using it and would really > appreciate fast fix: > > - UNNEST ARRAY with a nested ROW (described below, created ticket - > https://github.com/apache/beam/issues/26911) > > - PubSub table provider actually requires all table properties to be there > (with null in `timestampAttributeKey` it fails) - which essentially does > not allow one to use pubsub publish timestamp as `timestampAttributeKey`. > > - its not possible to cast VARCHAR to BYTES. And BYTES is needed for > DataStoreV1TableProvider to provide a key for storage. Also consider > updating `org.apache.beam.sdk.io.gcp.datastore.RowToEntity` so that it > requires VARCHAR instead of BYTES - its even easier in implementation. > > - Any hints on how to implement `FireStoreIOTableProvider`? I am > considering implementing it and contributing depending on my team decision > - but would like to get like idea how hard this task is. > > Will create tickets for the rest of issues when I will have some spare > time. > > Best regards > > Wiśniowski Piotr > > > On 22.05.2023 18:28, Alexey Romanenko wrote: > > Hi Piotr, > > Thanks for details! I cross-post this to dev@ as well since, I guess, > people there can provide more insights on this. > > A while ago, I faced the similar issues trying to run Beam SQL against > TPC-DS benchmark. > We had a discussion around that [1], please, take a look since it can be > helpful. > > [1] https://lists.apache.org/thread/tz8h1lycmob5vpkwznvc2g6ol2s6n99b > > — > Alexey > > On 18 May 2023, at 11:36, Wiśniowski Piotr > > wrote: > > HI, > > After experimenting with Beam SQL I did find some limitations. Testing on > near latest main (precisely `5aad2467469fafd2ed2dd89012bc80c0cd76b168`) > with Calcite, direct runner and openjdk version "11.0.19". Please let me > know if some of them are known/ worked on/ have tickets or have estimated > fix time. I believe most of them are low hanging fruits or just my thinking > is not right for the problem. If this is the case please guide me to some > working solution. > > From my perspective it is ok to have a fix just on master - no need to > wait for release. Priority order: > - 7. Windowing function on a stream - in detail - How to get previous > message for a key? setting expiration arbitrary big is ok, but access to > the previous record must happen fairly quickly not wait for the big window > to finish and emit the expired keys. Ideally would like to do it in pure > beam pipeline as saving to some external key/value store and then reading > this here could potentially result in some race conditions which in I would > like to avoid, but if its the only option - let it be. > - 5. single UNION ALL possible > - 4. UNNEST ARRAY with nested ROW > - 3. Using * when there is Row type present in the schema > - 1. `CROSS JOIN` between two unrelated tables is not supported - even if > one is a static number table > - 2. ROW construction not supported. It is not possible to nest data > > Below queries tat I use to testing this scenarios. > > Thank You for looking at this topics! > > Best > > Wiśniowski Piotr > --- > -- 1. `CROSS
Re: Beam SQL found limitations
Hi Alexey, Thank You for reference to that discussion I do actually have pretty similar thoughts on what Beam SQL needs. Update from my side: Actually did find a workaround for issue with windowing function on stream. It basically boils down to using sliding window to collect and aggregate the state. But would need an advice if this is actually a cost efficient method (targeting DataFlow runner). The doubt that I have is that this sliding window would need to have sliding interval less than 1s and size more than a week and be feed with quire frequent data. If I do understand this correctly - it would mean each input row would need to be duplicated for each window and stored which could be quite significant storage cost? Or actually Beam does not physically duplicate the record but just tracks to which windows the record currently belongs? And the real issue that BeamSQL needs at the moment in my opinion is fixing bugs. Some bugs that I found that prevent one from using it and would really appreciate fast fix: - UNNEST ARRAY with a nested ROW (described below, created ticket - https://github.com/apache/beam/issues/26911) - PubSub table provider actually requires all table properties to be there (with null in `timestampAttributeKey` it fails) - which essentially does not allow one to use pubsub publish timestamp as `timestampAttributeKey`. - its not possible to cast VARCHAR to BYTES. And BYTES is needed for DataStoreV1TableProvider to provide a key for storage. Also consider updating `org.apache.beam.sdk.io.gcp.datastore.RowToEntity` so that it requires VARCHAR instead of BYTES - its even easier in implementation. - Any hints on how to implement `FireStoreIOTableProvider`? I am considering implementing it and contributing depending on my team decision - but would like to get like idea how hard this task is. Will create tickets for the rest of issues when I will have some spare time. Best regards Wiśniowski Piotr On 22.05.2023 18:28, Alexey Romanenko wrote: Hi Piotr, Thanks for details! I cross-post this to dev@ as well since, I guess, people there can provide more insights on this. A while ago, I faced the similar issues trying to run Beam SQL against TPC-DS benchmark. We had a discussion around that [1], please, take a look since it can be helpful. [1] https://lists.apache.org/thread/tz8h1lycmob5vpkwznvc2g6ol2s6n99b — Alexey On 18 May 2023, at 11:36, Wiśniowski Piotr wrote: HI, After experimenting with Beam SQL I did find some limitations. Testing on near latest main (precisely `5aad2467469fafd2ed2dd89012bc80c0cd76b168`) with Calcite, direct runner and openjdk version "11.0.19". Please let me know if some of them are known/ worked on/ have tickets or have estimated fix time. I believe most of them are low hanging fruits or just my thinking is not right for the problem. If this is the case please guide me to some working solution. From my perspective it is ok to have a fix just on master - no need to wait for release. Priority order: - 7. Windowing function on a stream - in detail - How to get previous message for a key? setting expiration arbitrary big is ok, but access to the previous record must happen fairly quickly not wait for the big window to finish and emit the expired keys. Ideally would like to do it in pure beam pipeline as saving to some external key/value store and then reading this here could potentially result in some race conditions which in I would like to avoid, but if its the only option - let it be. - 5. single UNION ALL possible - 4. UNNEST ARRAY with nested ROW - 3. Using * when there is Row type present in the schema - 1. `CROSS JOIN` between two unrelated tables is not supported - even if one is a static number table - 2. ROW construction not supported. It is not possible to nest data Below queries tat I use to testing this scenarios. Thank You for looking at this topics! Best Wiśniowski Piotr --- -- 1. `CROSS JOIN` between two unrelated tables is not supported. --- -- Only supported is `CROSS JOIN UNNEST` when exploding array from same table. -- It is not possible to number rows WITHdata_table AS( SELECT1ASa ), number_table AS( SELECT numbers_exploded ASnumber_item FROMUNNEST(ARRAY[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]) ASnumbers_exploded ) SELECT data_table.a, number_table.number_item FROMdata_table CROSS JOINnumber_table ; -- CROSS JOIN, JOIN ON FALSE is not supported! --- -- 2. ROW construction not supported. It is not possible to nest data --- SELECTROW(1,2,'a') ASr; -- java.lang.NoSuchFieldException: EXPR$0 SELECT(1,2,'a') ASr; -- java.lang.NoSuchFieldException: EXPR$0 SELECTMAP['field1',1,'field2','a']; -- Parameters must be of the same type SELECTMAP['field1','b','field2','a']; -- null -- WORKAROUND - manually compose json string, -- drawback - decomposing might be not supported or would need to be
Re: Beam SQL found limitations
Hi Piotr,
Thanks for details! I cross-post this to dev@ as well since, I guess, people
there can provide more insights on this.
A while ago, I faced the similar issues trying to run Beam SQL against TPC-DS
benchmark.
We had a discussion around that [1], please, take a look since it can be
helpful.
[1] https://lists.apache.org/thread/tz8h1lycmob5vpkwznvc2g6ol2s6n99b
—
Alexey
> On 18 May 2023, at 11:36, Wiśniowski Piotr
> wrote:
>
> HI,
>
> After experimenting with Beam SQL I did find some limitations. Testing on
> near latest main (precisely `5aad2467469fafd2ed2dd89012bc80c0cd76b168`) with
> Calcite, direct runner and openjdk version "11.0.19". Please let me know if
> some of them are known/ worked on/ have tickets or have estimated fix time. I
> believe most of them are low hanging fruits or just my thinking is not right
> for the problem. If this is the case please guide me to some working solution.
>
> From my perspective it is ok to have a fix just on master - no need to wait
> for release. Priority order:
> - 7. Windowing function on a stream - in detail - How to get previous message
> for a key? setting expiration arbitrary big is ok, but access to the previous
> record must happen fairly quickly not wait for the big window to finish and
> emit the expired keys. Ideally would like to do it in pure beam pipeline as
> saving to some external key/value store and then reading this here could
> potentially result in some race conditions which in I would like to avoid,
> but if its the only option - let it be.
> - 5. single UNION ALL possible
> - 4. UNNEST ARRAY with nested ROW
> - 3. Using * when there is Row type present in the schema
> - 1. `CROSS JOIN` between two unrelated tables is not supported - even if one
> is a static number table
> - 2. ROW construction not supported. It is not possible to nest data
>
> Below queries tat I use to testing this scenarios.
>
> Thank You for looking at this topics!
>
> Best
>
> Wiśniowski Piotr
>
> ---
> -- 1. `CROSS JOIN` between two unrelated tables is not supported.
> ---
> -- Only supported is `CROSS JOIN UNNEST` when exploding array from same table.
> -- It is not possible to number rows
> WITH data_table AS (
> SELECT 1 AS a
> ),
> number_table AS (
> SELECT
> numbers_exploded AS number_item
> FROM UNNEST(ARRAY[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]) AS
> numbers_exploded
> )
> SELECT
> data_table.a,
> number_table.number_item
> FROM data_table
> CROSS JOIN number_table
> ;
> -- CROSS JOIN, JOIN ON FALSE is not supported!
>
>
> ---
> -- 2. ROW construction not supported. It is not possible to nest data
> ---
> SELECT ROW(1,2,'a') AS r; -- java.lang.NoSuchFieldException: EXPR$0
> SELECT (1,2,'a') AS r; -- java.lang.NoSuchFieldException: EXPR$0
> SELECT MAP['field1',1,'field2','a']; -- Parameters must be of the same type
> SELECT MAP['field1','b','field2','a']; -- null
> -- WORKAROUND - manually compose json string,
> -- drawback - decomposing might be not supported or would need to be also
> based on string operations
> SELECT ('{"field1":"' || 1 || '","field2":"' || 'a' || '"}') AS `json_object`;
>
>
> ---
> -- 3. Using * when there is Row type present in the schema
> ---
> CREATE EXTERNAL TABLE test_tmp_1(
> `ref` VARCHAR,
> `author` ROW<
> `name` VARCHAR,
> `email` VARCHAR
> >
> )
> TYPE text
> LOCATION 'python/dbt/tests/using_star_limitation.jsonl'
> TBLPROPERTIES '{"format":"json",
> "deadLetterFile":"top/python/dbt/tests/dead"}';
> SELECT * FROM test_tmp_1;
> -- java.lang.NoSuchFieldException: name
> -- WORKAROUND - refer to columns explicitly with alias
> SELECT
> `ref` AS ref_value,
> test_tmp_1.`author`.`name` AS author_name, -- table name must be
> referenced explicitly - this could be fixed too
> test_tmp_1.`author`.`email` AS author_name
> FROM test_tmp_1;
>
>
> ---
> -- 4. UNNEST ARRAY with nested ROW
> ---
> CREATE EXTERNAL TABLE test_tmp(
> `ref` VARCHAR,
> `commits` ARRAY `id` VARCHAR,
> `author` ROW<
> `name` VARCHAR,
> `email` VARCHAR
> >
> >>
> )
> TYPE text
> LOCATION 'python/dbt/tests/array_with_nested_rows_limitation.jsonl'
> TBLPROPERTIES '{"format":"json", "deadLetterFile":"python/dbt/tests/dead"}';
> SELECT
> test_tmp.`ref` AS branch_name,
> commit_item.`id` AS commit_hash,
> commit_item.`author`.`name` AS author_name
> FROM test_tmp
> CROSS JOIN UNNEST(test_tmp.commits) AS commit_item;
> -- Row expected 4 fields (Field{name=ref, description=, type=STRING,
> options={{}}}, Field{name=commits, description=, type=ARRAY author ROW> NOT NULL>, options={{}}},
> Field{name=id, description=, type=STRING, options={{}}}, Field{name=author,
> description=, type=ROW, options={{}}}).
>
