Scalability Considerations for Impala

Because Hadoop I/O is optimized for reading and writing large files, Impala is optimized for tables containing relatively few, large data files. Schemas containing thousands of tables, or tables containing thousands of partitions, can encounter performance issues during startup or during DDL operations such as ALTER TABLE statements.

Before Impala 2.1, the statestore sent only one kind of message to its subscribers. This message contained all updates for any topics that a subscriber had subscribed to. It also served to let subscribers know that the statestore had not failed, and conversely the statestore used the success of sending a heartbeat to a subscriber to decide whether or not the subscriber had failed.

Combining topic updates and failure detection in a single message led to bottlenecks in clusters with large numbers of tables, partitions, and HDFS data blocks. When the statestore was overloaded with metadata updates to transmit, heartbeat messages were sent less frequently, sometimes causing subscribers to time out their connection with the statestore. Increasing the subscriber timeout and decreasing the frequency of statestore heartbeats worked around the problem, but reduced responsiveness when the statestore failed or restarted.

As of Impala 2.1, the statestore now sends topic updates and heartbeats in separate messages. This allows the statestore to send and receive a steady stream of lightweight heartbeats, and removes the requirement to send topic updates according to a fixed schedule, reducing statestore network overhead.

The statestore now has the following relevant configuration flags for the statestored daemon:

-statestore_num_update_threads

The number of threads inside the statestore dedicated to sending topic updates. You should not typically need to change this value.

Default: 10

-statestore_update_frequency_ms

The frequency, in milliseconds, with which the statestore tries to send topic updates to each subscriber. This is a best-effort value; if the statestore is unable to meet this frequency, it sends topic updates as fast as it can. You should not typically need to change this value.

Default: 2000

-statestore_num_heartbeat_threads

The number of threads inside the statestore dedicated to sending heartbeats. You should not typically need to change this value.

Default: 10

-statestore_heartbeat_frequency_ms

The frequency, in milliseconds, with which the statestore tries to send heartbeats to each subscriber. This value should be good for large catalogs and clusters up to approximately 150 nodes. Beyond that, you might need to increase this value to make the interval longer between heartbeat messages.

Default: 1000 (one heartbeat message every second)

-statestore_heartbeat_tcp_timeout_seconds

The time after which a heartbeat RPC to a subscriber will timeout. This setting protects against badly hung machines that are not able to respond to the heartbeat RPC in short order. Increase this if there are intermittent heartbeat RPC timeouts shown in statestore's log. You can reference the max value of "statestore.priority-topic-update-durations" metric on statestore to get a reasonable value. Note that priority topic updates are assumed to be small amounts of data that take a small amount of time to process (similar to the heartbeat complexity).

Default: 3

-statestore_max_missed_heartbeats

Maximum number of consecutive heartbeat messages an impalad can miss before being declared failed by the statestore. You should not typically need to change this value.

Default: 10

-statestore_subscriber_timeout_secs

The amount of time (in seconds) that may elapse before the connection with the statestore is considered lost by subscribers (impalad/catalogd). Impalad will reregister itself to statestore, which may cause its absence in the next round of cluster membership update. This will cause query failures like "Cancelled due to unreachable impalad(s)". The value of this flag should be comparable to (statestore_heartbeat_frequency_ms / 1000 + statestore_heartbeat_tcp_timeout_seconds) * statestore_max_missed_heartbeats, so subscribers won't reregister themselves too early and allow statestore to resend heartbeats. You can also reference the max value of "statestore-subscriber.heartbeat-interval-time" metrics on impalads to get a reasonable value.

Default: 30

If it takes a very long time for a cluster to start up, and impala-shell consistently displays This Impala daemon is not ready to accept user requests, the statestore might be taking too long to send the entire catalog topic to the cluster. In this case, consider adding --load_catalog_in_background=false to your catalog service configuration. This setting stops the statestore from loading the entire catalog into memory at cluster startup. Instead, metadata for each table is loaded when the table is accessed for the first time.

The buffer pool feature, available in Impala 2.10 and higher, changes the way Impala allocates memory during a query. Most of the memory needed is reserved at the beginning of the query, avoiding cases where a query might run for a long time before failing with an out-of-memory error. The actual memory estimates and memory buffers are typically smaller than before, so that more queries can run concurrently or process larger volumes of data than previously.

The buffer pool feature includes some query options that you can fine-tune: BUFFER_POOL_LIMIT Query Option, DEFAULT_SPILLABLE_BUFFER_SIZE Query Option, MAX_ROW_SIZE Query Option, and MIN_SPILLABLE_BUFFER_SIZE Query Option.

Most of the effects of the buffer pool are transparent to you as an Impala user. Memory use during spilling is now steadier and more predictable, instead of increasing rapidly as more data is spilled to disk. The main change from a user perspective is the need to increase the MAX_ROW_SIZE query option setting when querying tables with columns containing long strings, many columns, or other combinations of factors that produce very large rows. If Impala encounters rows that are too large to process with the default query option settings, the query fails with an error message suggesting to increase the MAX_ROW_SIZE setting.

Certain memory-intensive operations write temporary data to disk (known as spilling to disk) when Impala is close to exceeding its memory limit on a particular host.

The result is a query that completes successfully, rather than failing with an out-of-memory error. The tradeoff is decreased performance due to the extra disk I/O to write the temporary data and read it back in. The slowdown could be potentially be significant. Thus, while this feature improves reliability, you should optimize your queries, system parameters, and hardware configuration to make this spilling a rare occurrence.

What kinds of queries might spill to disk:

Several SQL clauses and constructs require memory allocations that could activat the spilling mechanism:

• when a query uses a GROUP BY clause for columns with millions or billions of distinct values, Impala keeps a similar number of temporary results in memory, to accumulate the aggregate results for each value in the group.

• When large tables are joined together, Impala keeps the values of the join columns from one table in memory, to compare them to incoming values from the other table.

• When a large result set is sorted by the ORDER BY clause, each node sorts its portion of the result set in memory.

• The DISTINCT and UNION operators build in-memory data structures to represent all values found so far, to eliminate duplicates as the query progresses.

When the spill-to-disk feature is activated for a join node within a query, Impala does not produce any runtime filters for that join operation on that host. Other join nodes within the query are not affected.

How Impala handles scratch disk space for spilling:

By default, intermediate files used during large sort, join, aggregation, or analytic function operations are stored in the directory /tmp/impala-scratch, and these intermediate files are removed when the operation finishes. You can specify a different location by starting the impalad daemon with the ‑‑scratch_dirs="path_to_directory" configuration option.

Memory usage for SQL operators:

In Impala 2.10 and higher, the way SQL operators such as GROUP BY, DISTINCT, and joins, transition between using additional memory or activating the spill-to-disk feature is changed. The memory required to spill to disk is reserved up front, and you can examine it in the EXPLAIN plan when the EXPLAIN_LEVEL query option is set to 2 or higher.

The infrastructure of the spilling feature affects the way the affected SQL operators, such as GROUP BY, DISTINCT, and joins, use memory. On each host that participates in the query, each such operator in a query requires memory to store rows of data and other data structures. Impala reserves a certain amount of memory up front for each operator that supports spill-to-disk that is sufficient to execute the operator. If an operator accumulates more data than can fit in the reserved memory, it can either reserve more memory to continue processing data in memory or start spilling data to temporary scratch files on disk. Thus, operators with spill-to-disk support can adapt to different memory constraints by using however much memory is available to speed up execution, yet tolerate low memory conditions by spilling data to disk.

The amount data depends on the portion of the data being handled by that host, and thus the operator may end up consuming different amounts of memory on different hosts.

Added in: This feature was added to the ORDER BY clause in Impala 1.4. This feature was extended to cover join queries, aggregation functions, and analytic functions in Impala 2.0. The size of the memory work area required by each operator that spills was reduced from 512 megabytes to 256 megabytes in Impala 2.2. The spilling mechanism was reworked to take advantage of the Impala buffer pool feature and be more predictable and stable in Impala 2.10.

Avoiding queries that spill to disk:

Because the extra I/O can impose significant performance overhead on these types of queries, try to avoid this situation by using the following steps:

1. Detect how often queries spill to disk, and how much temporary data is written. Refer to the following sources:
   • The output of the PROFILE command in the impala-shell interpreter. This data shows the memory usage for each host and in total across the cluster. The WriteIoBytes counter reports how much data was written to disk for each operator during the query. (In Impala 2.9, the counter was named ScratchBytesWritten; in Impala 2.8 and earlier, it was named BytesWritten.)
   • The Queries tab in the Impala debug web user interface. Select the query to examine and click the corresponding Profile link. This data breaks down the memory usage for a single host within the cluster, the host whose web interface you are connected to.
2. Use one or more techniques to reduce the possibility of the queries spilling to disk:
   • Increase the Impala memory limit if practical, for example, if you can increase the available memory by more than the amount of temporary data written to disk on a particular node. Remember that in Impala 2.0 and later, you can issue SET MEM_LIMIT as a SQL statement, which lets you fine-tune the memory usage for queries from JDBC and ODBC applications.
   • Increase the number of nodes in the cluster, to increase the aggregate memory available to Impala and reduce the amount of memory required on each node.
   • Add more memory to the hosts running Impala daemons.
   • On a cluster with resources shared between Impala and other Hadoop components, use resource management features to allocate more memory for Impala. See Resource Management for details.
   • If the memory pressure is due to running many concurrent queries rather than a few memory-intensive ones, consider using the Impala admission control feature to lower the limit on the number of concurrent queries. By spacing out the most resource-intensive queries, you can avoid spikes in memory usage and improve overall response times. See Admission Control and Query Queuing for details.
   • Tune the queries with the highest memory requirements, using one or more of the following techniques:
     • Run the COMPUTE STATS statement for all tables involved in large-scale joins and aggregation queries.
     • Minimize your use of STRING columns in join columns. Prefer numeric values instead.
     • Examine the EXPLAIN plan to understand the execution strategy being used for the most resource-intensive queries. See Using the EXPLAIN Plan for Performance Tuning for details.
     • If Impala still chooses a suboptimal execution strategy even with statistics available, or if it is impractical to keep the statistics up to date for huge or rapidly changing tables, add hints to the most resource-intensive queries to select the right execution strategy. See Optimizer Hints for details.
   • If your queries experience substantial performance overhead due to spilling, enable the DISABLE_UNSAFE_SPILLS query option. This option prevents queries whose memory usage is likely to be exorbitant from spilling to disk. See DISABLE_UNSAFE_SPILLS Query Option (Impala 2.0 or higher only) for details. As you tune problematic queries using the preceding steps, fewer and fewer will be cancelled by this option setting.

Testing performance implications of spilling to disk:

To artificially provoke spilling, to test this feature and understand the performance implications, use a test environment with a memory limit of at least 2 GB. Issue the SET command with no arguments to check the current setting for the MEM_LIMIT query option. Set the query option DISABLE_UNSAFE_SPILLS=true. This option limits the spill-to-disk feature to prevent runaway disk usage from queries that are known in advance to be suboptimal. Within impala-shell, run a query that you expect to be memory-intensive, based on the criteria explained earlier. A self-join of a large table is a good candidate:

select count(*) from big_table a join big_table b using (column_with_many_values);

Issue the PROFILE command to get a detailed breakdown of the memory usage on each node during the query.

Set the MEM_LIMIT query option to a value that is smaller than the peak memory usage reported in the profile output. Now try the memory-intensive query again.

Check if the query fails with a message like the following:

WARNINGS: Spilling has been disabled for plans that do not have stats and are not hinted
to prevent potentially bad plans from using too many cluster resources. Compute stats on
these tables, hint the plan or disable this behavior via query options to enable spilling.

If so, the query could have consumed substantial temporary disk space, slowing down so much that it would not complete in any reasonable time. Rather than rely on the spill-to-disk feature in this case, issue the COMPUTE STATS statement for the table or tables in your sample query. Then run the query again, check the peak memory usage again in the PROFILE output, and adjust the memory limit again if necessary to be lower than the peak memory usage.

At this point, you have a query that is memory-intensive, but Impala can optimize it efficiently so that the memory usage is not exorbitant. You have set an artificial constraint through the MEM_LIMIT option so that the query would normally fail with an out-of-memory error. But the automatic spill-to-disk feature means that the query should actually succeed, at the expense of some extra disk I/O to read and write temporary work data.

Try the query again, and confirm that it succeeds. Examine the PROFILE output again. This time, look for lines of this form:

- SpilledPartitions: N

If you see any such lines with N greater than 0, that indicates the query would have failed in Impala releases prior to 2.0, but now it succeeded because of the spill-to-disk feature. Examine the total time taken by the AGGREGATION_NODE or other query fragments containing non-zero SpilledPartitions values. Compare the times to similar fragments that did not spill, for example in the PROFILE output when the same query is run with a higher memory limit. This gives you an idea of the performance penalty of the spill operation for a particular query with a particular memory limit. If you make the memory limit just a little lower than the peak memory usage, the query only needs to write a small amount of temporary data to disk. The lower you set the memory limit, the more temporary data is written and the slower the query becomes.

Now repeat this procedure for actual queries used in your environment. Use the DISABLE_UNSAFE_SPILLS setting to identify cases where queries used more memory than necessary due to lack of statistics on the relevant tables and columns, and issue COMPUTE STATS where necessary.

When to use DISABLE_UNSAFE_SPILLS:

You might wonder, why not leave DISABLE_UNSAFE_SPILLS turned on all the time. Whether and how frequently to use this option depends on your system environment and workload.

DISABLE_UNSAFE_SPILLS is suitable for an environment with ad hoc queries whose performance characteristics and memory usage are not known in advance. It prevents "worst-case scenario" queries that use large amounts of memory unnecessarily. Thus, you might turn this option on within a session while developing new SQL code, even though it is turned off for existing applications.

Organizations where table and column statistics are generally up-to-date might leave this option turned on all the time, again to avoid worst-case scenarios for untested queries or if a problem in the ETL pipeline results in a table with no statistics. Turning on DISABLE_UNSAFE_SPILLS lets you "fail fast" in this case and immediately gather statistics or tune the problematic queries.

Some organizations might leave this option turned off. For example, you might have tables large enough that the COMPUTE STATS takes substantial time to run, making it impractical to re-run after loading new data. If you have examined the EXPLAIN plans of your queries and know that they are operating efficiently, you might leave DISABLE_UNSAFE_SPILLS turned off. In that case, you know that any queries that spill will not go overboard with their memory consumption.

There are hardcoded limits on the maximum size and complexity of queries. Currently, the maximum number of expressions in a query is 2000. You might exceed the limits with large or deeply nested queries produced by business intelligence tools or other query generators.

If you have the ability to customize such queries or the query generation logic that produces them, replace sequences of repetitive expressions with single operators such as IN or BETWEEN that can represent multiple values or ranges. For example, instead of a large number of OR clauses:

WHERE val = 1 OR val = 2 OR val = 6 OR val = 100 ...

use a single IN clause:

WHERE val IN (1,2,6,100,...)

Impala parallelizes its I/O operations aggressively, therefore the more disks you can attach to each host, the better. Impala retrieves data from disk so quickly using bulk read operations on large blocks, that most queries are CPU-bound rather than I/O-bound.

Because the kind of sequential scanning typically done by Impala queries does not benefit much from the random-access capabilities of SSDs, spinning disks typically provide the most cost-effective kind of storage for Impala data, with little or no performance penalty as compared to SSDs.

Resource management features such as YARN, Llama, and admission control typically constrain the amount of memory, CPU, or overall number of queries in a high-concurrency environment. Currently, there is no throttling mechanism for Impala I/O.

Due to the overhead of retrieving and updating table metadata in the metastore database, try to limit the number of columns in a table to a maximum of approximately 2000. Although Impala can handle wider tables than this, the metastore overhead can become significant, leading to query performance that is slower than expected based on the actual data volume.

To minimize overhead related to the metastore database and Impala query planning, try to limit the number of partitions for any partitioned table to a few tens of thousands.

If the volume of data within a table makes it impractical to run exploratory queries, consider using the TABLESAMPLE clause to limit query processing to only a percentage of data within the table. This technique reduces the overhead for query startup, I/O to read the data, and the amount of network, CPU, and memory needed to process intermediate results during the query. See TABLESAMPLE Clause for details.

When Impala starts up, or after each kinit refresh, Impala sends a number of simultaneous requests to the KDC. For a cluster with 100 hosts, the KDC might be able to process all the requests within roughly 5 seconds. For a cluster with 1000 hosts, the time to process the requests would be roughly 500 seconds. Impala also makes a number of DNS requests at the same time as these Kerberos-related requests.

While these authentication requests are being processed, any submitted Impala queries will fail. During this period, the KDC and DNS may be slow to respond to requests from components other than Impala, so other secure services might be affected temporarily.

In Impala 2.12 or earlier, to reduce the frequency of the kinit renewal that initiates a new set of authentication requests, increase the kerberos_reinit_interval configuration setting for the impalad daemons. Currently, the default is 60 minutes. Consider using a higher value such as 360 (6 hours).

The kerberos_reinit_interval configuration setting is removed in Impala 3.0, and the above step is no longer needed.

You can use the HDFS caching feature, described in Using HDFS Caching with Impala (Impala 2.1 or higher only), with Impala to reduce I/O and memory-to-memory copying for frequently accessed tables or partitions.

In the early days of this feature, you might have found that enabling HDFS caching resulted in little or no performance improvement, because it could result in "hotspots": instead of the I/O to read the table data being parallelized across the cluster, the I/O was reduced but the CPU load to process the data blocks might be concentrated on a single host.

To avoid hotspots, include the WITH REPLICATION clause with the CREATE TABLE or ALTER TABLE statements for tables that use HDFS caching. This clause allows more than one host to cache the relevant data blocks, so the CPU load can be shared, reducing the load on any one host. See CREATE TABLE Statement and ALTER TABLE Statement for details.

Hotspots with high CPU load for HDFS cached data could still arise in some cases, due to the way that Impala schedules the work of processing data blocks on different hosts. In Impala 2.5 and higher, scheduling improvements mean that the work for HDFS cached data is divided better among all the hosts that have cached replicas for a particular data block. When more than one host has a cached replica for a data block, Impala assigns the work of processing that block to whichever host has done the least work (in terms of number of bytes read) for the current query. If hotspots persist even with this load-based scheduling algorithm, you can enable the query option SCHEDULE_RANDOM_REPLICA=TRUE to further distribute the CPU load. This setting causes Impala to randomly pick a host to process a cached data block if the scheduling algorithm encounters a tie when deciding which host has done the least work.

One scalability aspect that affects heavily loaded clusters is the load on the metadata layer from looking up the details as each file is opened. On HDFS, that can lead to increased load on the NameNode, and on S3, this can lead to an excessive number of S3 metadata requests. For example, a query that does a full table scan on a partitioned table may need to read thousands of partitions, each partition containing multiple data files. Accessing each column of a Parquet file also involves a separate "open" call, further increasing the load on the NameNode. High NameNode overhead can add startup time (that is, increase latency) to Impala queries, and reduce overall throughput for non-Impala workloads that also require accessing HDFS files.

You can reduce the number of calls made to your file system's metadata layer by enabling the file handle caching feature. Data files that are accessed by different queries, or even multiple times within the same query, can be accessed without a new "open" call and without fetching the file details multiple times.

The feature is enabled by default with 20,000 file handles to be cached. To change the value, set the configuration option max_cached_file_handles to a non-zero value for each impalad daemon. From the initial default value of 20000, adjust upward if NameNode request load is still significant, or downward if it is more important to reduce the extra memory usage on each host. Each cache entry consumes 6 KB, meaning that caching 20,000 file handles requires up to 120 MB on each Impala executor. The exact memory usage varies depending on how many file handles have actually been cached; memory is freed as file handles are evicted from the cache.

If a manual operation moves a file to the trashcan while the file handle is cached, Impala still accesses the contents of that file. This is a change from prior behavior. Previously, accessing a file that was in the trashcan would cause an error. This behavior only applies to non-Impala methods of removing files, not the Impala mechanisms such as TRUNCATE TABLE or DROP TABLE.

If files are removed, replaced, or appended by operations outside of Impala, the way to bring the file information up to date is to run the REFRESH statement on the table.

File handle cache entries are evicted as the cache fills up, or based on a timeout period when they have not been accessed for some time.

To evaluate the effectiveness of file handle caching for a particular workload, issue the PROFILE statement in impala-shell or examine query profiles in the Impala Web UI. Look for the ratio of CachedFileHandlesHitCount (ideally, should be high) to CachedFileHandlesMissCount (ideally, should be low). Before starting any evaluation, run several representative queries to "warm up" the cache because the first time each data file is accessed is always recorded as a cache miss.

To see metrics about file handle caching for each impalad instance, examine the following fields on the /metrics page in the Impala Web UI:

• impala-server.io.mgr.cached-file-handles-miss-count
• impala-server.io.mgr.num-cached-file-handles