f86b24001f
* Add top-level `Conn`. Fixes #296. This is a little different than the API rnewman and I originally discussed in https://public.etherpad-mozilla.org/p/db-conn-thoughts. A few notes: - I was led to make a `Schema` instance the thing that is shared, rather than a `db::DB`. It's possible that queries will want to know the current transaction at some point (to prevent races, or to query historical data), but that can be a future consideration. - The generation number just allows for a cheap comparison. I don't care to handle races to transact just yet; the long term plan might be to make embedding applications responsible for avoiding races, or we might handle queuing transactions and yielding report futures in Mentat itself. - The sharing of the partition maps is a little more subtle than expected. Partition maps are volatile: a successful Mentat transaction always advances the :db.part/tx partition, so it's not worth passing references around. This means that consumers must clone in order to maintain just a single clone per transaction. Clean some cruft. * Review comments.
364 lines
17 KiB
Rust
364 lines
17 KiB
Rust
// Copyright 2016 Mozilla
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//
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// Licensed under the Apache License, Version 2.0 (the "License"); you may not use
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// this file except in compliance with the License. You may obtain a copy of the
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// License at http://www.apache.org/licenses/LICENSE-2.0
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// Unless required by applicable law or agreed to in writing, software distributed
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// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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// CONDITIONS OF ANY KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations under the License.
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#![allow(dead_code)]
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//! This module implements the transaction application algorithm described at
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//! https://github.com/mozilla/mentat/wiki/Transacting and its children pages.
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//!
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//! The implementation proceeds in four main stages, labeled "Pipeline stage 1" through "Pipeline
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//! stage 4". _Pipeline_ may be a misnomer, since the stages as written **cannot** be interleaved
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//! in parallel. That is, a single transacted entity cannot flow through all the stages without its
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//! sibling entities.
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//!
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//! This unintuitive architectural decision was made because the second and third stages (resolving
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//! lookup refs and tempids, respectively) operate _in bulk_ to minimize the number of expensive
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//! SQLite queries by processing many in one SQLite invocation. Pipeline stage 2 doesn't need to
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//! operate like this: it is easy to handle each transacted entity independently of all the others
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//! (and earlier, less efficient, implementations did this). However, Pipeline stage 3 appears to
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//! require processing multiple elements at the same time, since there can be arbitrarily complex
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//! graph relationships between tempids. Pipeline stage 4 (inserting elements into the SQL store)
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//! could also be expressed as an independent operation per transacted entity, but there are
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//! non-trivial uniqueness relationships inside a single transaction that need to enforced.
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//! Therefore, some multi-entity processing is required, and a per-entity pipeline becomes less
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//! attractive.
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//!
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//! A note on the types in the implementation. The pipeline stages are strongly typed: each stage
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//! accepts and produces a subset of the previous. We hope this will reduce errors as data moves
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//! through the system. In contrast the Clojure implementation rewrote the fundamental entity type
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//! in place and suffered bugs where particular code paths missed cases.
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//!
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//! The type hierarchy accepts `Entity` instances from the transaction parser and flows `Term`
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//! instances through the term-rewriting transaction applier. `Term` is a general `[:db/add e a v]`
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//! with restrictions on the `e` and `v` components. The hierarchy is expressed using `Result` to
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//! model either/or, and layers of `Result` are stripped -- we might say the `Term` instances are
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//! _lowered_ as they flow through the pipeline. This type hierarchy could have been expressed by
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//! combinatorially increasing `enum` cases, but this makes it difficult to handle the `e` and `v`
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//! components symmetrically. Hence, layers of `Result` type aliases. Hopefully the explanatory
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//! names -- `TermWithTempIdsAndLookupRefs`, anyone? -- and strongly typed stage functions will help
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//! keep everything straight.
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use std;
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use std::borrow::Cow;
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use std::collections::BTreeSet;
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use ::{to_namespaced_keyword};
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use db;
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use db::{
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MentatStoring,
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PartitionMapping,
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};
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use entids;
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use errors::{ErrorKind, Result};
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use internal_types::{
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LookupRefOrTempId,
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TempId,
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TempIdMap,
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Term,
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TermWithTempIdsAndLookupRefs,
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TermWithTempIds,
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TermWithoutTempIds,
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replace_lookup_ref};
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use mentat_core::{
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intern_set,
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Schema,
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};
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use mentat_tx::entities as entmod;
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use mentat_tx::entities::{Entity, OpType};
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use rusqlite;
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use schema::{
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SchemaBuilding,
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SchemaTypeChecking,
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};
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use types::{
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Attribute,
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AVPair,
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AVMap,
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Entid,
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PartitionMap,
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TypedValue,
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TxReport,
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ValueType,
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};
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use upsert_resolution::Generation;
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/// A transaction on its way to being applied.
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#[derive(Debug)]
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pub struct Tx<'conn, 'a> {
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/// The storage to apply against. In the future, this will be a Mentat connection.
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store: &'conn rusqlite::Connection, // TODO: db::MentatStoring,
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/// The partition map to allocate entids from.
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///
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/// The partition map is volatile in the sense that every succesful transaction updates
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/// allocates at least one tx ID, so we own and modify our own partition map.
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partition_map: PartitionMap,
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/// The schema to use when interpreting the transaction entities.
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///
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/// The schema is infrequently updated, so we borrow a schema until we need to modify it.
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schema: Cow<'a, Schema>,
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/// The transaction ID of the transaction.
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tx_id: Entid,
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/// The timestamp when the transaction began to be committed.
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///
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/// This is milliseconds after the Unix epoch according to the transactor's local clock.
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// TODO: :db.type/instant.
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tx_instant: i64,
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}
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impl<'conn, 'a> Tx<'conn, 'a> {
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pub fn new(store: &'conn rusqlite::Connection, partition_map: PartitionMap, schema: &'a Schema, tx_id: Entid, tx_instant: i64) -> Tx<'conn, 'a> {
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Tx {
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store: store,
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partition_map: partition_map,
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schema: Cow::Borrowed(schema),
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tx_id: tx_id,
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tx_instant: tx_instant,
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}
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}
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/// Given a collection of tempids and the [a v] pairs that they might upsert to, resolve exactly
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/// which [a v] pairs do upsert to entids, and map each tempid that upserts to the upserted
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/// entid. The keys of the resulting map are exactly those tempids that upserted.
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pub fn resolve_temp_id_avs<'b>(&self, temp_id_avs: &'b [(TempId, AVPair)]) -> Result<TempIdMap> {
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if temp_id_avs.is_empty() {
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return Ok(TempIdMap::default());
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}
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// Map [a v]->entid.
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let mut av_pairs: Vec<&AVPair> = vec![];
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for i in 0..temp_id_avs.len() {
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av_pairs.push(&temp_id_avs[i].1);
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}
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// Lookup in the store.
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let av_map: AVMap = self.store.resolve_avs(&av_pairs[..])?;
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// Map id->entid.
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let mut temp_id_map: TempIdMap = TempIdMap::default();
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for &(ref temp_id, ref av_pair) in temp_id_avs {
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if let Some(n) = av_map.get(&av_pair) {
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if let Some(previous_n) = temp_id_map.get(&*temp_id) {
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if n != previous_n {
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// Conflicting upsert! TODO: collect conflicts and give more details on what failed this transaction.
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bail!(ErrorKind::NotYetImplemented(format!("Conflicting upsert: tempid '{}' resolves to more than one entid: {:?}, {:?}", temp_id, previous_n, n))) // XXX
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}
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}
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temp_id_map.insert(temp_id.clone(), *n);
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}
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}
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Ok((temp_id_map))
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}
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/// Pipeline stage 1: convert `Entity` instances into `Term` instances, ready for term
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/// rewriting.
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///
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/// The `Term` instances produce share interned TempId and LookupRef handles.
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fn entities_into_terms_with_temp_ids_and_lookup_refs<I>(&self, entities: I) -> Result<Vec<TermWithTempIdsAndLookupRefs>> where I: IntoIterator<Item=Entity> {
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let mut temp_ids = intern_set::InternSet::new();
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entities.into_iter()
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.map(|entity: Entity| -> Result<TermWithTempIdsAndLookupRefs> {
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match entity {
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Entity::AddOrRetract { op, e, a, v } => {
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let a: i64 = match a {
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entmod::Entid::Entid(ref a) => *a,
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entmod::Entid::Ident(ref a) => self.schema.require_entid(&a)?,
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};
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let attribute: &Attribute = self.schema.require_attribute_for_entid(a)?;
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let e = match e {
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entmod::EntidOrLookupRefOrTempId::Entid(e) => {
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let e: i64 = match e {
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entmod::Entid::Entid(ref e) => *e,
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entmod::Entid::Ident(ref e) => self.schema.require_entid(&e)?,
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};
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std::result::Result::Ok(e)
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},
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entmod::EntidOrLookupRefOrTempId::TempId(e) => {
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std::result::Result::Err(LookupRefOrTempId::TempId(temp_ids.intern(e)))
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},
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entmod::EntidOrLookupRefOrTempId::LookupRef(_) => {
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// TODO: reference entity and initial input.
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bail!(ErrorKind::NotYetImplemented(format!("Transacting lookup-refs is not yet implemented")))
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},
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};
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let v = {
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if attribute.value_type == ValueType::Ref && v.is_text() {
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std::result::Result::Err(LookupRefOrTempId::TempId(temp_ids.intern(v.as_text().unwrap().clone())))
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} else if attribute.value_type == ValueType::Ref && v.is_vector() && v.as_vector().unwrap().len() == 2 {
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bail!(ErrorKind::NotYetImplemented(format!("Transacting lookup-refs is not yet implemented")))
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} else {
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// Here is where we do schema-aware typechecking: we either assert that
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// the given value is in the attribute's value set, or (in limited
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// cases) coerce the value into the attribute's value set.
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let typed_value: TypedValue = self.schema.to_typed_value(&v, &attribute)?;
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std::result::Result::Ok(typed_value)
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}
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};
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Ok(Term::AddOrRetract(op, e, a, v))
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},
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}
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})
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.collect::<Result<Vec<_>>>()
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}
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/// Pipeline stage 2: rewrite `Term` instances with lookup refs into `Term` instances without
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/// lookup refs.
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///
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/// The `Term` instances produce share interned TempId handles and have no LookupRef references.
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fn resolve_lookup_refs<I>(&self, lookup_ref_map: &AVMap, terms: I) -> Result<Vec<TermWithTempIds>> where I: IntoIterator<Item=TermWithTempIdsAndLookupRefs> {
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terms.into_iter().map(|term: TermWithTempIdsAndLookupRefs| -> Result<TermWithTempIds> {
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match term {
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Term::AddOrRetract(op, e, a, v) => {
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let e = replace_lookup_ref(&lookup_ref_map, e, |x| x)?;
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let v = replace_lookup_ref(&lookup_ref_map, v, |x| TypedValue::Ref(x))?;
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Ok(Term::AddOrRetract(op, e, a, v))
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},
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}
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}).collect::<Result<Vec<_>>>()
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}
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/// Transact the given `entities` against the store.
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///
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/// This approach is explained in https://github.com/mozilla/mentat/wiki/Transacting.
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// TODO: move this to the transactor layer.
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pub fn transact_entities<I>(&mut self, entities: I) -> Result<TxReport> where I: IntoIterator<Item=Entity> {
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// TODO: push these into an internal transaction report?
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// We don't yet support lookup refs, so this isn't mutable. Later, it'll be mutable.
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let lookup_refs: intern_set::InternSet<AVPair> = intern_set::InternSet::new();
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// TODO: extract the tempids set as well.
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// Pipeline stage 1: entities -> terms with tempids and lookup refs.
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let terms_with_temp_ids_and_lookup_refs = self.entities_into_terms_with_temp_ids_and_lookup_refs(entities)?;
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// Pipeline stage 2: resolve lookup refs -> terms with tempids.
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let lookup_ref_avs: Vec<&(i64, TypedValue)> = lookup_refs.inner.iter().map(|rc| &**rc).collect();
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let lookup_ref_map: AVMap = self.store.resolve_avs(&lookup_ref_avs[..])?;
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let terms_with_temp_ids = self.resolve_lookup_refs(&lookup_ref_map, terms_with_temp_ids_and_lookup_refs)?;
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// Pipeline stage 3: upsert tempids -> terms without tempids or lookup refs.
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// Now we can collect upsert populations.
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let (mut generation, inert_terms) = Generation::from(terms_with_temp_ids, &self.schema)?;
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// And evolve them forward.
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while generation.can_evolve() {
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// Evolve further.
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let temp_id_map = self.resolve_temp_id_avs(&generation.temp_id_avs()[..])?;
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generation = generation.evolve_one_step(&temp_id_map);
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}
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// Allocate entids for tempids that didn't upsert. BTreeSet rather than HashSet so this is deterministic.
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let unresolved_temp_ids: BTreeSet<TempId> = generation.temp_ids_in_allocations();
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// TODO: track partitions for temporary IDs.
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let entids = self.partition_map.allocate_entids(":db.part/user", unresolved_temp_ids.len());
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let temp_id_allocations: TempIdMap = unresolved_temp_ids.into_iter().zip(entids).collect();
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let final_populations = generation.into_final_populations(&temp_id_allocations)?;
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/// Assertions that are :db.cardinality/one and not :db.fulltext.
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let mut non_fts_one: Vec<db::ReducedEntity> = vec![];
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/// Assertions that are :db.cardinality/many and not :db.fulltext.
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let mut non_fts_many: Vec<db::ReducedEntity> = vec![];
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let final_terms: Vec<TermWithoutTempIds> = [final_populations.resolved,
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final_populations.allocated,
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inert_terms.into_iter().map(|term| term.unwrap()).collect()].concat();
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// Pipeline stage 4: final terms (after rewriting) -> DB insertions.
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// Collect into non_fts_*.
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// TODO: use something like Clojure's group_by to do this.
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for term in final_terms {
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match term {
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Term::AddOrRetract(op, e, a, v) => {
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let attribute: &Attribute = self.schema.require_attribute_for_entid(a)?;
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if attribute.fulltext {
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bail!(ErrorKind::NotYetImplemented(format!("Transacting :db/fulltext entities is not yet implemented"))) // TODO: reference original input. Difficult!
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}
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let added = op == OpType::Add;
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if attribute.multival {
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non_fts_many.push((e, a, attribute, v, added));
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} else {
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non_fts_one.push((e, a, attribute, v, added));
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}
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},
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}
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}
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// Transact [:db/add :db/txInstant NOW :db/tx].
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// TODO: allow this to be present in the transaction data.
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non_fts_one.push((self.tx_id,
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entids::DB_TX_INSTANT,
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// TODO: extract this to a constant.
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self.schema.require_attribute_for_entid(self.schema.require_entid(&to_namespaced_keyword(":db/txInstant").unwrap())?)?,
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TypedValue::Long(self.tx_instant),
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true));
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if !non_fts_one.is_empty() {
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self.store.insert_non_fts_searches(&non_fts_one[..], db::SearchType::Inexact)?;
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}
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if !non_fts_many.is_empty() {
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self.store.insert_non_fts_searches(&non_fts_many[..], db::SearchType::Exact)?;
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}
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self.store.commit_transaction(self.tx_id)?;
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// TODO: update idents and schema materialized views.
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db::update_partition_map(self.store, &self.partition_map)?;
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Ok(TxReport {
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tx_id: self.tx_id,
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tx_instant: self.tx_instant,
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})
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}
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}
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/// Transact the given `entities` against the given SQLite `conn`, using the given metadata.
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///
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/// This approach is explained in https://github.com/mozilla/mentat/wiki/Transacting.
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// TODO: move this to the transactor layer.
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pub fn transact<'conn, 'a, I>(conn: &'conn rusqlite::Connection, mut partition_map: PartitionMap, schema: &'a Schema, entities: I) -> Result<(TxReport, PartitionMap, Option<Schema>)> where I: IntoIterator<Item=Entity> {
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// Eventually, this function will be responsible for managing a SQLite transaction. For
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// now, it's just about the tx details.
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let tx_instant = ::now(); // Label the transaction with the timestamp when we first see it: leading edge.
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let tx_id = partition_map.allocate_entid(":db.part/tx");
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conn.begin_transaction()?;
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let mut tx = Tx::new(conn, partition_map, schema, tx_id, tx_instant);
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let report = tx.transact_entities(entities)?;
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// If the schema has moved on, return it.
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let next_schema = match tx.schema {
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Cow::Borrowed(_) => None,
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Cow::Owned(next_schema) => Some(next_schema),
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};
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Ok((report, tx.partition_map, next_schema))
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}
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