Scalability Considerations

Impact of Many Tables or Partitions on Impala Catalog Performance and Memory Usage

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.

Scalability Considerations for the Impala StateStore

If it takes a very long time for a cluster to start up with the message, 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 setting the Load Catalog in Background field to false in 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.

Effect of Buffer Pool on Memory Usage

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.

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.

SQL Operations that Spill to Disk

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.

What kinds of queries might spill to disk:

Several SQL clauses and constructs require memory allocations that could trigger spilling to disk:

• 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.

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.

Memory usage for SQL operators:

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.

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 of 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.

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 Queries in Cloudera Manager, you can see the peak memory usage for a query, combined across all nodes in the cluster.
   • In 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, using the SET MEM_LIMIT SQL statement, increase the available memory by more than the amount of temporary data written to disk on a particular node.
   • 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.
   • On a cluster with resources shared between Impala and other Hadoop components, use resource management features to allocate more memory for Impala.
   • 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.
   • 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.
     • 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.
   • 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. As you tune problematic queries using the preceding steps, fewer and fewer will be cancelled by this option setting.

When to use DISABLE_UNSAFE_SPILLS:

The DISABLE_UNSAFE_SPILLS query option 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.

Limits on Query Size and Complexity

There are hard-coded 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,...)

Scalability Considerations for Impala I/O

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.

Scalability Considerations for Table Layout

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.

Kerberos-Related Network Overhead for Large Clusters

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.

Avoiding CPU Hotspots for HDFS Cached Data

You can use the HDFS caching feature to reduce I/O and memory-to-memory copying for frequently accessed tables or partitions.

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.

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.

Scalability Considerations for File Handle Caching

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 Maximum Cached File Handles (max_cached_file_handles) to a non-zero value for each Impala daemon (impalad). 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