* feat: parquet cache metrics
* feat: track parquet cache metrics
Adds metrics to track the following in the in-memory parquet cache:
* cache size in bytes (also included a fix in the calculation of that)
* cache size in n files
* cache hits
* cache misses
* cache misses while the oracle is fetching a file
A test was added to check this functionality
* refactor: clean up logic and fix cache removal tracking error
Some logic and naming was cleaned up and the boolean to optionally track
metrics on entry removal was removed, as it was incorrect in the first place:
a fetching entry still has a size, which counts toward the size of the
cache. So, this makes is such that anytime an entry is removed, whether
its state is success or fetching, its size will be decremented from
the cache size metrics.
The sizing caclulations were made to be correct, and the cache metrics
test was updated with more thurough assertions
Moved all of the last cache implementation into the `influxdb3_cache`
crate. This also splits out the implementation into three modules:
- `cache.rs`: the core cache implementation
- `provider.rs`: the cache provider used by the database to hold multiple
caches.
- `table_function.rs`: same as before, holds the DataFusion impls
Tests were preserved and moved to `mod.rs`, however, they were updated to
not rely on the WriteBuffer implementation, and instead use the types in
the `influxdb3_cache::last_cache` module directly. This simplified the
test code, while not changing any of the test assertions at all.
This commit does three important major changes:
1. We will deny writes to the v1, v2, and v3 write apis that add new tags in
subsequent writes after the first write
2. We make every table have a series key by default now
3. We enfore sorting order by the series key which is the order the keys came in
With these changes we have consistentcy across the various write apis and can
make optimizations and future features with the assumption we have a series key.
Closes#25585
This commit introduces basic store for sys events and the backing ring
buffer. Since the buffer needs to hold arbitrary data, it uses `Box<dyn
Any>`
closes: https://github.com/influxdata/influxdb/issues/25581
This adds two new CLI commands to the `influxdb3` binary:
* `influxdb3 meta-cache create`
* `influxdb3 meta-cache delete`
To create and delete metadata caches, respectively.
A basic integration test was added to check that this works E2E.
The `influxdb3_client` was updated with methods to create and delete
metadata caches, and which is what the CLI commands use under the hood.
This adds a new system table "meta_caches" that allows users to view the
state of their metadata caches on a per-db basis
An integration test was added to verify that it works.
This commit allows deleting (soft) a table. For an user, following
command will allow soft deleting a table (bar) in db (foo)
```
influxdb3 table delete --dbname foo --table bar --host $host
```
- Added `soft_delete_table` to `DatabaseManager` trait, which already
hosts `soft_delete_database` method. The code roughly follows the same
flow as db delete. Although like db schema, it does clone on write
because the reference is behind an Arc, `Arc::make_mut` is used in
this change.
- Moved db delete related cli parser under "manage" module that has both
db and table delete functionality
- Some minor tidyups (removing unused methods, renaming method so that
the order in name matches actual return type eg. `table_id_and_schema`,
should return (id, schema) and not (schema, id))
closes: https://github.com/influxdata/influxdb/issues/25561
* feat: drop/delete database
This commit allows soft deletion of database using `influxdb3 database
delete <db_name>` command. The write buffer and last value cache are
cleared as well.
closes: https://github.com/influxdata/influxdb/issues/25523
* feat: reuse same code path when deleting database
- In previous commit, the deletion of database immediately triggered
clearing last cache and query buffer. But on restarts same logic had
to be repeated to allow deleting database when starting up. This
commit removes immediate deletion by explicitly calling necessary
methods and moves the logic to `apply_catalog_batch` which already
applies `CatalogOp` and also clearing cache and buffer in
`buffer_ops` method which has hooks to call other places.
closes: https://github.com/influxdata/influxdb/issues/25523
* feat: use reqwest query api for query param
Co-authored-by: Trevor Hilton <thilton@influxdata.com>
* feat: include deleted flag in DatabaseSnapshot
- `DatabaseSchema` serialization/deserialization is delegated to
`DatabaseSnapshot`, so the `deleted` flag should be included in
`DatabaseSnapshot` as well.
- insta test snapshots fixed
closes: https://github.com/influxdata/influxdb/issues/25523
* feat: address PR comments + tidy ups
---------
Co-authored-by: Trevor Hilton <thilton@influxdata.com>
* refactor: make last cache eviction optional
This changes how the last cache is evicted. It will no longer run eviction
on writes to the cache, instead, there is an optional method to create a
last cache provider that will run eviction in a background task on a specified
interval.
Otherwise, when records are produced from the cache, only those that have
not expired will be produced.
This should reduce locks on the cache and hopefully improve performance.
* feat: configurable last cache eviction interval
* docs: clean up var names, code docs, and comments
Closes#25461
_Note: the first three commits on this PR are from https://github.com/influxdata/influxdb/pull/25492_
This PR makes the switch from using names for columns to the use of `ColumnId`s. Where column names are used, they are represented as `Arc<str>`. This impacts most components of the system, and the result is a fairly sizeable change set. The area where the most refactoring was needed was in the last-n-value cache.
One of the themes of this PR is to rely less on the arrow `Schema` for handling the column-level information, and tracking that info in our own `ColumnDefinition` type, which captures the `ColumnId`.
I will summarize the various changes in the PR below, and also leave some comments in-line in the PR.
## Switch to `u32` for `ColumnId`
The `ColumnId` now follows the `DbId` and `TableId`, and uses a globally unique `u32` to identify all columns in the database. This was a change from using a `u16` that was only unique within the column's table. This makes it easier to follow the patterns used for creating the other identifier types when dealing with columns, and should reduce the burden of having to manage the state of a table-scoped identifier.
## Changes in the WAL/Catalog
* `WriteBatch` now contains no names for tables or columns and purely uses IDs
* This PR relies on `IndexMap` for `_Id`-keyed maps so that the order of elements in the map is consistent. This has important implications, namely, that when iterating over an ID map, the elements therein will always be produced in the same order which allows us to make assertions on column order in a lot of our tests, and allows for the re-introduction of `insta` snapshots for serialization tests. This map type provides O(1) lookups, but also provides _fast_ iteration, which should help when serializing these maps in write batches to the WAL.
* Removed the need to serialize the bi-directional maps for `DatabaseSchema`/`TableDefinition` via use of `SerdeVecMap` (see comments in-line)
* The `tables` map in `DatabaseSchema` no stores an `Arc<TableDefinition>` so that the table definition can be shared around more easily. This meant that changes to tables in the catalog need to do a clone, but we were already having to do a clone for changes to the DB schema.
* Removal of the `TableSchema` type and consolidation of its parts/functions directly onto `TableDefinition`
* Added the `ColumnDefinition` type, which represents all we need to know about a column, and is used in place of the Arrow `Schema` for column-level meta-info. We were previously relying heavily on the `Schema` for iterating over columns, accessing data types, etc., but this gives us an API that we have more control over for our needs. The `Schema` is still held at the `TableDefinition` level, as it is needed for the query path, and is maintained to be consistent with what is contained in the `ColumnDefinition`s for a table.
## Changes in the Last-N-Value Cache
* There is a bigger distinction between caches that have an explicit set of value columns, and those that accept new fields. The former should be more performant.
* The Arrow `Schema` is managed differently now: it used to be updated more than it needed to be, and now is only updated when a row with new fields is pushed to a cache that accepts new fields.
## Changes in the write-path
* When ingesting, during validation, field names are qualified to their associated column ID
* refactor: roll back addition of DatabaseSchemaProvider trait
* refactor: make parquet metrics optional in telemetry for pro
* refactor: make ParquetFileId Hash
* refactor: test harness logging
Allow the endpoint for telemetry to be passed in via the cli args, e.g
```
--telemetry-endpoint "https://somehost/test/"
```
and the actual endpoint always appends `v3` to it. So, above URL becomes
"https://somehost/test/v3"
Separate out methods of the Catalog API that are used on the query side into a new trait `DatabaseSchemaProvider`. The new trait includes methods from the Catalog that get the underlying `DatabaseSchema` or interact with names/IDs.
This will allow for a separate implementation of the Catalog for pro that only needs to hold a replicated/combined view in-memory of one or more catalogs without the need to do persistence that a write buffer's catalog needs to do.
While in there I also switched the `QueryExecutorImpl::new` method to take an args struct to avoid the clippy lint.
* feat: Add TableId and ColumnId
* feat: swap over to DbId and TableId everywhere
This commit swaps us over to using the DbId and TableId types everywhere
for our internal systems. Anywhere that's external facing, such as names
for last cache tables or line protocol parsing, use names. In these cases
we have the `Catalog` which keeps a map of TableIds and DbIds in a
bidirectional mapping for easy lookup i.e. id <-> names. While in essence
the change itself isn't that complicated given the nature of how much we
depended on names for things, the changes end up being quite invasive and
extensive. Luckily it shouldn't be too hard to review. Note this does
not add the column ids which will be done in a follow up PR.
Closes#25375Closes#25403Closes#25404Closes#25405Closes#25412Closes#25413
- added mechanism within PersistedFile to expose parquet file related
metrics. The details are updated when new snapshot is generated and
also when all snapshots are loaded when the process starts up
- at the point of creating the telemetry payload these parquet metrics
are looked up before sending it to the server.
Closes: https://github.com/influxdata/influxdb/issues/25418
- instrumented code to get read and write measurement
- introduced EventsBucket for collection of reads/writes
- sampler now samples every minute for all metrics (including
reads/writes)
- other tidy ups
closes: https://github.com/influxdata/influxdb/issues/25372
* test: check parquet cache in the write buffer
Checked that the parquet cache will serve queries when chunks are
requested from the write buffer. The added test also checks for get_range
requests made to the object store, which are typically made by DataFusion
to infer schema for parquet files.
* refactor: make parquet cache optional on write buffer
* test: add test to verify parquet cache function
This makes the parquet cache optional at the write buffer level, and adds
a test that verifies that the cache catches and prevents requests to the
object store in the event of a cache hit.
Closes#25382Closes#25383
This refactors the parquet cache to use less locking by switching from using the `clru` crate to a hand-rolled cache implementation. The new cache still acts as an LRU, but it uses atomics to track hit-time per entry, and handles pruning in a separate process that is decoupled from insertion/gets to the cache.
The `Cache` type uses a [`DashMap`](https://docs.rs/dashmap/latest/dashmap/struct.DashMap.html) internally to store cache entries. This should help reduce lock contention, and also has the added benefit of not requiring mutability to insert into _or_ get from the map.
The cache maps an `object_store::Path` to a `CacheEntry`. On a hit, an entry will have its `hit_time` (an `AtomicI64`) incremented. During a prune operation, entries that have the oldest hit times will be removed from the cache. See the `Cache::prune` method for details.
The cache is setup with a memory _capacity_ and a _prune percent_. The cache tracks memory used when entries are added, based on their _size_, and when a prune is invoked in the background, if the cache has exceeded its capacity, it will prune `prune_percent * cache.len()` entries from the cache.
Two tests were added:
* `cache_evicts_lru_when_full` to check LRU behaviour of the cache
* `cache_hit_while_fetching` to check that a cache entry hit while a request is in flight to fetch that entry will not result in extra calls to the underlying object store
Part of #25347
This sets up a new implementation of an in-memory parquet file cache in the `influxdb3_write` crate in the `parquet_cache.rs` module.
This module introduces the following types:
* `MemCachedObjectStore` - a wrapper around an `Arc<dyn ObjectStore>` that can serve GET-style requests to the store from an in-memory cache
* `ParquetCacheOracle` - an interface (trait) that can accept requests to create new cache entries in the cache used by the `MemCachedObjectStore`
* `MemCacheOracle` - implementation of the `ParquetCacheOracle` trait
## `MemCachedObjectStore`
This takes inspiration from the [`MemCacheObjectStore` type](1eaa4ed5ea/object_store_mem_cache/src/store.rs (L205-L213)) in core, but has some different semantics around its implementation of the `ObjectStore` trait, and uses a different cache implementation.
The reason for wrapping the object store is that this ensures that any GET-style request being made for a given object is served by the cache, e.g., metadata requests made by DataFusion.
The internal cache comes from the [`clru` crate](https://crates.io/crates/clru), which provides a least-recently used (LRU) cache implementation that allows for weighted entries. The cache is initialized with a capacity and entries are given a weight on insert to the cache that represents how much of the allotted capacity they will take up. If there isn't enough room for a new entry on insert, then the LRU item will be removed.
### Limitations of `clru`
The `clru` crate conveniently gives us an LRU eviction policy but its API may put some limitations on the system:
* gets to the cache require an `&mut` reference, which means that the cache needs to be behind a `Mutex`. If this slows down requests through the object store, then we may need to explore alternatives.
* we may want more sophisticated eviction policies than a straight LRU, i.e., to favour certain tables over others, or files that represent recent data over those that represent old data.
## `ParquetCacheOracle` / `MemCacheOracle`
The cache oracle is responsible for handling cache requests, i.e., to fetch an item and store it in the cache. In this PR, the oracle runs a background task to handle these requests. I defined this as a trait/struct pair since the implementation may look different in Pro vs. OSS.
- uses Arc<str> to represent create once and read everywhere type
of string
- updated snapshots for insta asserts, uses redaction to hardcode
randomly generated UUID strings
- added methods to catalog to expose instace and host ids
Closes: https://github.com/influxdata/influxdb/issues/25315
This makes some changes to the TestServer E2E framework, which is used
for running integration tests in the influxdb3 crate. These changes are
meant so that we can more easily split the code for pro.
* refactor: add catalog as dep to influxdb3
* refactor: move catalog and last cache initialization out of write buffer
The Write buffer used to handle initialization of the catalog and last
n value cache. This commit moves that logic out, so that both can be
initialized independently, and injected into the write buffer. This is to
enable downstream changes that will need to make sharing the catalog and
last cache possible.
* refactor: use dyn traits in WriteBufferImpl
This changes the WriteBufferImpl to use a dyn TimeProvider instead of
a generic in its type signature.
The Server type now uses a dyn WriteBuffer instead of using a generic
in its type signature, and the ServerBuilder was updated to accommodate
this accordingly.
These chages were to make downstream code changes more seamless.
* refactor: make some items pub
This makes functions on the QueryableBuffer and LastCache pub so that they
can be used downstream.
The Persister trait was only implemented by a single type, because the
underlying ObjectStore interface has several ways of being mocked, we
mock that instead of the Persister interface.
This commit removes the Persister trait, and moves its interface/impl
directly on a single Persister type in the persister module of the
influxdb3_write crate.
deny.toml had some incorrect field names in license.exceptions, those
were fixed from 'crate' to 'name'.
* refactor: Make Level0Duration part of WAL
I noticed this during some testing and cleanup with other PRs. The WAL had its own level_0_duration and the write buffer had a different one, which would cause some weird problems if they weren't the same. This refactors Level0Duration to be in the WAL and fixes up the tests.
As an added bonus, this surfaced a bug where multiple L0 blocks getting persisted in the same snapshot wasn't supported. So now snapshot details can have many files per table.
* fix: have persisted files always return in descending data time order
* fix: sort record batches for test verification
This extends the system tables available with a new `parquet_files` table
which will list the parquet files associated with a given table in a
database.
Queries to system.parquet_files must provide a table_name predicate to
specify the table name of interest.
The files are accessed through the QueryableBuffer.
In addition, a test was added to check success and failure modes of the
new system table query.
Finally, the Persister trait had its associated error type removed. This
was somewhat of a consequence of how I initially implemented this change,
but I felt cleaned the code up a bit, so I kept it in the commit.
This enforces the use of a host identifier prefix in all object store
paths (currently, for parquet files, catalog files, and snapshot files).
The persister retains the host identifier prefix, and uses it when
constructing paths.
The WalObjectStore also holds the host identifier prefix, so that it can
use it when saving and loading WAL files.
The influxdb3 binary requires a new argument 'host-id' to be passed that
is used to specify the prefix.
* fix: query bugs with buffer
This fixes three different bugs with the buffer. First was that aggregations would fail because projection was pushed down to the in-buffer data that de-duplication needs to be called on. The test in influxdb3/tests/server/query.rs catches that.
I also added a test in write_buffer/mod.rs to ensure that data is correctly queryable when combining with different states: only data in buffer, only data in parquet files, and data across both. This showed two bugs, one where the parquet data was being doubled up (parquet chunks were being created in write buffer mod and in queryable buffer. The second was that the timestamp min max on table buffer would panic if the buffer was empty.
* refactor: PR feedback
* fix: fix wal replay and buffer snapshot
Fixes two problems uncovered by adding to the write_buffer/mod.rs test. Ensures we can replay wal data and that snapshots work properly with replayed data.
* fix: run cargo update to fix audit
* feat: refactor WAL and WriteBuffer
There is a ton going on here, but here are the high level things. This implements a new WAL, which is backed entirely by object store. It then updates the WriteBuffer to be able to work with how the new WAL works, which also required an update to how the Catalog is modified and persisted.
The concept of Segments has been removed. Previously there was a separate WAL per segment of time. Instead, there is now a single WAL that all writes and updates flow into. Data within the write buffer is organized by Chunk(s) within tables, which is based on the timestamp of the row data. These are known as the Level0 files, which will be persisted as Parquet into object store. The default chunk duration for level 0 files is 10 minutes.
The WAL is written as single files that get created at the configured WAL flush interval (1s by default). After a certain number of files have been created, the server will attempt to snapshot the WAL (default is to snapshot the first 600 files of the WAL after we have 900 total, i.e. snapshot 10 minutes of WAL data).
The design goal with this is to persist 10 minute chunks of data that are no longer receiving writes, while clearing out old WAL files. This works if data getting written in around "now" with no more than 5 minutes of delay. If we continue to have delayed writes, a snapshot of all data will be forced in order to clear out the WAL and free up memory in the buffer.
Overall, this structure of a single wal, with flushes and snapshots and chunks in the queryable buffer led to a simpler setup for the write buffer overall. I was able to clear out quite a bit of code related to the old segment organization.
Fixes#25142 and fixes#25173
* refactor: address PR feedback
* refactor: wal to replay and background flush on new
* chore: remove stray println
This commit updates us to rustc 1.80. There are three significant changes
here:
1. LazyLock and LazyCell have been stabilized meaning we can replace our
usage of Lazy from the once_cell crate with the std lib versions
2. Lints were added to handle unknown cfg directives. `tokio_unstable`
is affected by this and while we do have the flags in our
.cargo/config.toml Cargo still output a lint for it so we supress
that warning now in our Cargo.toml for the workspace
3. clippy now throws a new warning about priority levels for lints. It's
quite frankly a thing that doesn't make sense to me and should be
something cargo fixes, but here we are.
Besides that it was a painless upgrade and now we're on the latest and
greatest.
* fix: catalog support for last caches that accept new fields
Last cache definitions in the catalog were augmented to either store an
explicit set of column names (including time), or to accept new fields.
This will allow these caches to be loaded properly on server restart such
that all non-key columns are cached.
* refactor: use tagged serialization for last cache values def
This also updated the client code to accept the new structure in
influxdb3_client.
* test: add e2e tests to catch regressions in influxdb3_client
* chore: cargo update for audit
Closes#25169
This PR ensures the last cache configuration is persisted to the catalog when last caches are created, and are removed from the catalog when they are deleted. The last cache is initialized on server start fro the catalog.
A new trait was added to the write buffer: LastCacheManager, which provides the methods to create and delete last caches (and which is invoked from the HTTP API). Both create/delete methods will update the catalog, but also force persistence of the catalog to object store, vs. waiting for the WAL flush interval / segment persistence process to do it. This should ensure that the catalog is up-to-date with respect to the last cache configuration, in the event that the server is stopped before segment persistence.
A test was added to check this behaviour in influxdb3_write/src/write_buffer/mod.rs.
* feat: new last-cache CLI
This adds two new CLIs:
influxdb3 last-cache create
influxdb3 last-cache delete
These utilize the new underlying APIs/client methods for the last-n-value
cache feature.
* refactor: switch around the token CLI to new convention
* docs: re-word CLI docs
Added a new system table, system.last_caches, to enable queries that display information about last caches in a database.
You can query the table like so:
SELECT * FROM system.last_caches
Since queries are scoped to a database, this will only show last caches configured for the database being queried.
Results look like so:
+-------+----------------+----------------+---------------+-------+-----+
| table | name | key_columns | value_columns | count | ttl |
+-------+----------------+----------------+---------------+-------+-----+
| mem | mem_last_cache | [host, region] | [time, usage] | 1 | 60 |
+-------+----------------+----------------+---------------+-------+-----+
An end-to-end test was added to verify queries to the system.last_caches table.
Trevor Hilton
Michael Gattozzi
praveen-influx
Paul Dix