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…s needs to be fixed
…ing stuff, and sadly includes the other patch as well..
…e the threshold at which late materialization is triggered
…constructor is called
…ack of it with an atomic
This is already in the auto-install list, should be in the auto-load list as well
…ack of it with an atomic (duckdb#15798)
…orage version when serializing a database (duckdb#15794) Follow-up from duckdb#15702 Supersedes/builds on top of duckdb#14981 This PR change the `storage_compatibility_version` from being a setting set on every session to be written in the database file. Previously we would set this setting at run-time, and it would be shared across all database instances: ```sql ATTACH 'file1.db'; -- write something, to be serialized targeting version v0.10.0 SET storage_compatibility_version = 'v1.0.0'; ATTACH 'file2.db'; -- write something, to be serialized targeting v1.0.0 ``` This has a number of issues: * The storage compatibility version is shared across all attached databases * When restarting the system, the `storage_compatibility_version` would revert back towards the default setting (currently `v0.10.0`) * When reading a database, we did not know which storage compatibility version was used, which could lead to hard to understand errors when reading databases with an older version ### STORAGE_VERSION parameter This PR reworks this so that the storage version is instead specified on `ATTACH`. When none is specified: * The version set in the `storage_compatibility_version` is used when creating a new database * The version stored within the database is used when loading an existing database As a result, we can target the storage version towards the desired supported version when creating a new database. When opening an existing database, we will keep on writing targeting the same DuckDB version (i.e. we never automatically "upgrade" the file to a newer DuckDB version). The user can *manually* upgrade a file by opening an older file while targeting a later storage version. For example: ```sql -- use default `storage_compatibility_version` ATTACH 'new_file.db'; -- explicitly target versions >= v1.2.0 ATTACH 'new_file.db' (STORAGE_VERSION 'v1.2.0'); -- use the storage version stored within the file ATTACH 'existing_file.db'; -- use storage version v1.2.0 - if the file uses an older storage version, this upgrades the file ATTACH 'existing_file.db' (STORAGE_VERSION 'v1.2.0'); ``` Note that we cannot *downgrade* a file. If we try to open a file that targets e.g. version v1.2.0 with an explicit storage version of v1.0.0, we get an error: ```sql ATTACH 'database_file.db' (STORAGE_VERSION 'v1.2.0'); DETACH database_file; ATTACH 'database_file.db' (STORAGE_VERSION 'v1.0.0'); -- Error opening "database_file.db": cannot initialize database with storage version 2 - which is lower than what the database itself uses (4). The storage version of an existing database cannot be lowered. ``` ### Opening with DuckDB < v1.1.3 When opening a file that targets `v1.2.0` in an older DuckDB version, we now get a storage incompatibility error: ```sql duckdb database_file.db ``` ``` Error: unable to open database "database_file.db": IO Error: Trying to read a database file with version number 65, but we can only read version 64. The database file was created with an newer version of DuckDB. The storage of DuckDB is not yet stable; newer versions of DuckDB cannot read old database files and vice versa. The storage will be stabilized when version 1.0 releases. For now, we recommend that you load the database file in a supported version of DuckDB, and use the EXPORT DATABASE command followed by IMPORT DATABASE on the current version of DuckDB. See the storage page for more information: https://duckdb.org/internals/storage ``` The description in the error is not entirely correct - but the error is a lot more descriptive than the previous error that would be thrown in this scenario (which was `INTERNAL Error: Unsupported compression function type`). The error message has also been improved in duckdb#15702 already.
These tests were inadvertendly disabled because the name of the require flag changed sometime back.
These tests ``` test/sql/json/test_json_copy_tpch.test_slow -- 1 test/sql/aggregate/group/test_group_by_parallel.test_slow -- 2 test/sql/parallelism/intraquery/depth_first_evaluation.test_slow -- 3 ``` Were giving memory/storage issues in CI. For (1.), I've reduced the TPC-H scale factor to 0.1 instead of 1. We're still testing what we want to test, so this shouldn't be an issue (TPC-H files are really large when exported to JSON). For (2.), the setting `PRAGMA verify_external;` was triggering a lot of partitioning and spilling to test the external grouped aggregation behavior. It was spilling more than necessary because the HT size calculation was off a bit, especially when this setting was enabled. For (3.), a slight increase to the memory limit in the test was enough to make it work again. We're still testing the behavior we want to.
This PR is a follow-up from duckdb#14175 that changed the return value of `map_extract` and `element_at` to return a single value from maps by key instead of a list with the value. This is a good change, but unfortunately It breaks backwards/forwards compatibility for serialized query plans. This PR fixes it by reverting the change to `map_extract` but instead add a new `map_extract_value` with the new behavior. It also changes the syntax sugar (e.g. `[]`) to use this new function instead, so the end user still "mostly" sees the new behavior, while the system internally still keeps the same behavior for the actual function.
* SelectionVector(start, count) should respect the count when initializing * `ColumnFetchState` needs to be transaction-local, not part of the global state
* Big ROW_GROUP_SIZE require explict STORAGE_VERSION * ALTERNATIVE_VERIFY=1 implies default to be `latest`, so test do not make much sense
These tests were inadvertently disabled because the name of the require flag changed sometime back. Note: The same should be checked on all extension tests (I think a test from the fts extension has the same issue).
Tmonster
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…groups, and limiting vacuum operations for the last number of row groups (duckdb#18829) When performing a checkpoint, we rewrite columns of row groups that have had modifications. When performing insertions, all columns are modified, thus all columns of a row group must be rewritten. As a result, any insertion followed by a checkpoint triggers a full rewrite of the last row group. When dealing with wide tables or string columns with large strings, rewriting a row group can be relatively expensive. This is particularly the case when only small insertions have been made. For example, inserting a single row followed by checkpointing can become expensive as a result of triggering a rewrite of the last row group: ```sql create table z as select repeat(sha256(i::varchar), 10) as a, repeat(sha256((i**2)::varchar), 10) as b, repeat(sha256((i**3)::varchar), 10) as c from range(100000) r(i); CHECKPOINT; .timer on insert into z values ('a','b','c'); Run Time (s): real 0.006 user 0.004249 sys 0.001186 CHECKPOINT; Run Time (s): real 0.611 user 1.163009 sys 0.054814 ``` The checkpoint takes 0.6s, despite us inserting only a single row. #### Appending a new row group This PR solves this problem by appending a new row group when writing to a table that has been persisted/checkpointed. As a result, we will no longer modify the (already checkpointed) data. As a trade-off, we end up with more (smaller) row groups. #### Vacuuming As part of vacuuming, we merge adjacent row groups if they fit within the row group size. As part of this PR - we restrict merging for the last few row groups. Otherwise, we would end up with the same problem. We would create row groups like so: ``` 100K rows 1 row ``` Then merge them back into a single row group with size `100K+1`, effectively performing the same rewrite as before. Instead, for the last row groups in a file, we need a *minimum threshold* to merge. This is either the `row group size` itself, or 2X the size of the first row group we are considering. We also always merge row groups if the total size is `< 2048` (the vector size). This exponential merging ensures we don't merge on every single insert, while also ensuring we never have too many row groups. We can see this process in action when starting with 100K rows, and inserting 100 rows at a time - effectively repeating the below script: ```sql insert into z select 'a','b','c' from range(100); checkpoint; select row_group_id, max(count) from pragma_storage_info('z') group by all order by all; ``` ```sql -- insert batch #1 ┌──────────────┬────────────┐ │ row_group_id │ max(count) │ │ int64 │ int64 │ ├──────────────┼────────────┤ │ 0 │ 100000 │ │ 1 │ 100 │ └──────────────┴────────────┘ -- insert batch #30 ┌──────────────┬────────────┐ │ row_group_id │ max(count) │ │ int64 │ int64 │ ├──────────────┼────────────┤ │ 0 │ 100000 │ │ 1 │ 2000 │ │ 2 │ 1000 │ └──────────────┴────────────┘ -- insert batch #150 ┌──────────────┬────────────┐ │ row_group_id │ max(count) │ │ int64 │ int64 │ ├──────────────┼────────────┤ │ 0 │ 100000 │ │ 1 │ 8000 │ │ 2 │ 4000 │ │ 3 │ 2000 │ │ 4 │ 1000 │ └──────────────┴────────────┘ -- insert batch #228 ┌──────────────┬────────────┐ │ row_group_id │ max(count) │ │ int64 │ int64 │ ├──────────────┼────────────┤ │ 0 │ 100000 │ │ 1 │ 16000 │ │ 2 │ 4000 │ │ 3 │ 2000 │ │ 4 │ 800 │ └──────────────┴────────────┘ -- insert batch #229 ┌──────────────┬────────────┐ │ row_group_id │ max(count) │ │ int64 │ int64 │ ├──────────────┼────────────┤ │ 0 │ 122800 │ │ 1 │ 100 │ └──────────────┴────────────┘ ``` ### Performance Running the above example again, the checkpoint now completes in `0.005` seconds, instead of `0.6` seconds. Note that we still do the compaction at some point, so while most checkpoints will have gotten faster, not all of them will have gotten faster. Running the above example 300X, here are the timings: ```sql create table z as select repeat(sha256(i::varchar), 10) as a, repeat(sha256((i**2)::varchar), 10) as b, repeat(sha256((i**3)::varchar), 10) as c from range(100000) r(i); -- 300X the below insertion insert into z select 'a','b','c' from range(100); CHECKPOINT; ``` | Summary | v1.3.2 | New | |-----------------------------|---------|--------| | Total Time | 128.82s | 1.00s | | Average Time Per Checkpoint | 0.56s | 0.01s | | Max Checkpoint Time | 0.631s | 0.526s | In the above example, there is still a single checkpoint that does the full on compaction, and thus has the same timing as the original script had for every checkpoint.
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This PR adds support for left join reordering to the join order optimizer.
This PR includes the following pieces of logic
(Select * from (select * from t1, t2) left join (select * from t3, t4) on (a = d))have projections above the left and right subqueries. Currently, the Join Order Optimizer treats Projections as stand along relations and optimizes the children in isolation of the rest of the plan. In cases like this however, the projection is not necessary and is only added as a part of the process to plan a subquery.I ran tpcds on this branch at sf100 and two queries that stood out were q40 and q80