Overview

This work is to solve the consistency problem if we use HMS HA with metadata cache. Note it does not aim to address any existing consistency issues already exist in non-cached HMS. For example, it won’t fix the transaction semantic between metadata and data. If the problem exists today in non-cached HMS, it stays a problem after this work.

The problem we try to solve here is the cache consistency issue. We already build a HMS cache to cache the metadata. If we have multiple HMS in the cluster, the cache is not synchronized. That is, if metastore 1 changed a table/partition, metastore 2 won’t see the change immediately. There’s a background thread keep polling from the notification log and update changed entries, so the cache is eventually consistent. In this work, we want to make the cache full consistent. The idea is at read time, we will check if the cached entry is obsolete or not. However, we don’t want to penalize the read performance. We don’t want to introduce additional db call to compare the db version and cached version in order to do the check. The solution is we will use the transaction state of a query for the version check. A query will pull the transaction state of involved tables (ValidWriteIdList) from db (non-cached) anyway. So we don’t need additional db call to check the staleness of the cache.

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Data structure change

The only data structure change is adding ValidWriteIdList into SharedCache.TableWrapper, which represents the transaction state of the cached table.

Note there is no db table structure change, and we don’t store extra information in db. We don’t update TBLS.WRITE_ID field as we will use db as the fact of truth. We assume db always carry the latest copy and every time we fetch from db, we will tag it with the transaction state of the query.

How It Works

Read

Metastore read request will compare ValidWriteIdList parameter with the cached one. If the cached version is fresh or newer (there’s no transaction committed after the entry is cached) , Metastore will use cached copy. Otherwise, Metastore will retrieve the entry from ObjectStore.

Here is the example for a get_table request:

1. At the beginning of the query, Hive will retrieve the global transaction state and store in config (ValidTxnList.VALID_TXNS_KEY)
2. Hive translate ValidTxnList to ValidWriteIdList of the table [12:7,8,12] (The format for writeid is [hwm:exceptions], all writeids from 1 to hwm minus exceptions, are committed. In this example, writeid 1..6,9,10,11 are committed)
3. Hive pass the ValidWriteIdList to HMS
4. HMS compare ValidWriteIdList [12:7,8,12] with the cached one [11:7,8] using TxnIdUtils.compare, if it is fresh or newer (Fresh or newer means no transaction committed between two states. In this example, [11:7,8] means writeid 1..6,9,10,11 are committed, the same as the requested writeid [12:7,8,12]), HMS return cached table entry
5. If the cached ValidWriteIdList is [12:7,12], the comparison fails because writeid 8 is committed since then. HMS will fetch the table from ObjectStore
6. HMS will eventually catch up with the newer version from notification log. HMS will serve the request from cache since then

Here is another example of get_partitions_by_expr. The API is a list query not a point lookup. There are a couple of similarities and differences to point out:

1. HMS will still compare requested writeid and cached table writeid to decide if the request can serve from cache
2. Every add/remove/alter/rename partition request will increment the table writeid. HMS will mark cached table entry invalid upon processing the first write message from notification log, and mark it valid and tag with the right writeid upon processing the commit message from notification log

Write

There is no change on HMS write side. HMS will write data into db and also put an entry in notification log.

Commit

When the transaction is committed, HMS will put writeid of the modified tables during the query into notification log. HMS will retrieve the writeid from db. As an optimization, HMS client may also pass a flag to indicate this is a read-only transaction, HMS would not pull writeid from db if it is read-only.

Cache update

In the previous discussion, we know if the cache is stale, HMS will serve the request from ObjectStore. We need to catch up the cache with the latest change. This can be done by the existing notification log based cache update mechanism. A thread in HMS constantly poll from notification log, update the cache with the entries from notification log. The interesting entries in notification log are table/partition writes, and corresponding commit transaction message. When processing table/partition writes, HMS will put the table/partition entry in cache. However, the entry is not immediately usable until the commit message of the corresponding writes is processed, and tag the ValidWriteIdList for all the entries modified by the transaction.

Here is a complete flow for a cache update when write happen (and illustrated in the diagram):

1. The ValidWriteIdList of cached table is initially [11:7,8]
2. HMS 1 get a alter_table request. HMS 1 puts alter_table message to notification log
3. The transaction in HMS 1 get committed. HMS 1 puts commit message to notification log along with the writeid [12:7,8]
4. The cache update thread in HMS 2 will read the alter_table event from notification log, update the cache with the new version from notification log. However, the entry is not available for read as there’s no writeid associate with it yet
5. A read for the entry on HMS 2 will fetch from db since the entry is not available for read
6. The cache update thread will further read commit event from notification log, tag the entry with the writeid [12:7,8]
7. The next read from HMS 2 will serve from cache

Bootstrap

The use cases discussed so far are driven by a query. However, during the HMS startup, there’s a cache prewarm. HMS will fetch everything from db to cache. There is no particular query drives the process, that means we don’t have ValidWriteIdList of the query. Prewarm needs to generate ValidWriteIdList by itself. To do that, for every table, HMS will query the current global transaction state ValidTxnList (HiveTxnManager.getValidTxns), and then convert it to table specific ValidWriteIdList (HiveTxnManager.getValidWriteIds). As an optimization, we don’t have to invoke HiveTxnManager.getValidTxns per table. We can invoke it every couple of minutes. If ValidTxnList is outdated, we will get an outdated ValidWriteIdList. Next time when Hive read this entry, Metastore will fetch from the db even though it is in fact fresh. There’s no correctness issue, only impact performance in some cases. The other possibility is the entry changes after we fetches ValidWriteIdList. This is not unlikely as fetching all partitions of the table may take some time. If that happens, the cached entry is actually newer than the ValidWriteIdList. The next time Hive reads it will trigger a db read though it is not necessary. Again, there’s no correctness issue, only impact performance in some cases.

External Table

Write id is not valid for external tables. And Hive won’t fetch ValidWriteIdList if the query source is external tables. Without ValidWriteIdList, HMS won’t able to check the staleness of the cache. To solve this problem, we will use the original eventually consistent model for external tables. That is, if the table is external table, Hive will pass null ValidWriteIdList to metastore API/CachedStore. Metastore cache won’t store ValidWriteIdList alongside the entry. When reading, CachedStore always retrieve the current copy. The original notification update will update the metadata of external tables, so we can eventually get updates from external changes.

Consistency Guarantee

Since the source of truth for cache is notification log, and the notification log is total ordered, the cache provides monotonic reads. The cost of that is we delay the update of cache until the notification log is caught up by the background thread. This guarantee the cache always move forward not backward. During the interim before HMS catch up the notification log, read will be served from db. The performance will suffer during this short period of time, but consider write operation is less often, the cost is minor.

The other benefit of this approach is it provide right semantic even if a transaction consists of more than one HMS write request. In that case, there are multiple copies of the cache entry could associate with a single writeid. For example, if there are two alter table request in the transaction, one request route to HMS 1 and the other route to HMS 2. We might not be able to tell which copy is applicable for this writeid if not carefully designed. By using notification log and only make cache available upon commit message, we can make sure we apply all request of the transaction. Once the commit message is processed, we can make sure the copy we have in cache is correct after this transaction.

The other thing to note is this approach does not provide read your own writes. Reads within the transaction cannot request newest copy which contains its own write, since the transaction is not committed thus the cached copy may not be correct. It will be correct until HMS apply all writes of the transaction from notification log. So the reads might end up reading the old copy from cache (consider the next read route to the other HMS). I am not sure if there’s a use case read the entry just written in Hive. If it does, we need to cache the written copy in client, so the next time client reads, it will be retrieved from client cache to make sure it contains its own writes.

API changes

When reading, we need to pass a writeid list so HMS can compare it with the cached version. For every commit, we need to tell HMS what is the writeid for the tables changed during the transaction, so HMS can correctly tag the entry with the writeid. We need to add ValidWriteIdList into metastore thrift API and RawStore interfaces, including all table/partition related read calls and commit transaction calls.

Hive_metastore.thrift and HiveMetaStoreClient.java

Adding a serialized version of ValidWriteIdList to every read HMS API.

Actually we don’t need to add the new field into every read request because:

1. Many APIs are using a request structure rather than taking individual parameters. So need to add ValidWriteIdList to the request structure instead
2. Some APIs already take ValidWriteIdList to invalidate outdated transactional statistics. We don’t need to change the API signature, but will reuse the ValidWriteIdList to validate cached entries in CachedStore

HMS read API will remain backward compatible for external table. That is, new server can deal with old client. If the old client issue a create_table call, server side will receive the request of create_table with validWriteIdList=null, and will cache or retrieve the entry regardless(with eventual consistency model). For managed table, validWriteIdList will be required and HMS server will throw an exception if validWriteIdList=null.

In commit_txn request, we will add an optional boolean field writemanaged, which indicate the query modifies managed tables in the cache. If this is false, HMS won’t fetch writeid for the transaction from db. Note this is a performance optimization which does not impact the correctness.

RawStore

ObjectStore will use the additional validWriteIdList field for all read methods to compare with cached ValidWriteIdList

Hive.java

The implementation details will be encapsulated in Hive.java. Which include:

1. Generate new write id for every write operation involving managed tables. Since DbTxnManager cache write id for every transaction, so every query will generate at most one new write id for a single table, even if it consists of multiple Hive.java write API calls
2. Retrieve table write id from config for every read operation if exists (for managed table, it guarantees to be there in config), and pass the write id to HMS API

Changes in Other Projects

All other components invoking HMS API directly (bypass Hive.java) will be changed to invoke the newer HMS API. This includes HCatalog, Hive streaming, etc, and other projects using HMS client such as Impala.

For every read request involving table/partitions, HMS client need to pass a validWriteIdList string in addition to the existing arguments. validWriteIdList can be null if it is external table, as HMS will return whatever in the cache for external table using eventual consistency. But if validWriteIdList=null for managed table, HMS will throw exception. validWriteIdList is a serialized form of ValidReaderWriteIdList. Usually ValidReaderWriteIdList can be obtained from HiveTxnManager using the following code snippet:

ValidTxnList txnIds = txnMgr.getValidTxns(); // get global transaction state
ValidTxnWriteIdList txnWriteIds = txnMgr.getValidWriteIds(txnTables, txnString); // map global transaction state to table specific write id

Optionally, HMS client (HiveMetaStoreClient) can set writemanaged flag in commit transaction request (commitTxn) if this transaction modifies any managed table/partition. This will save a db fetch for HMS for readonly query or DDL/DML for external tables. If this set to true wrongly (eg, readonly query claim it modifies managed table), there will be a performance penalty processing commit message. If this set to false wrongly (eg, DDL on managed table claim it does not touch managed table), the entry in the cache will not mark available thus every read has to go to db. In both scenarios, there is no correctness issue.