Palo is an MPP-based interactive SQL data warehousing for reporting and analysis. Palo mainly integrates the technology of Google Mesa and Apache Impala. Unlike other popular SQL-on-Hadoop systems, Palo is designed to be a simple and single tightly coupled system, not depending on other systems. Palo not only provides high concurrent low latency point query performance, but also provides high throughput queries of ad-hoc analysis. Palo not only provides batch data loading, but also provides near real-time mini-batch data loading. Palo also provides high availability, reliability, fault tolerance, and scalability. The simplicity (of developing, deploying and using) and meeting many data serving requirements in single system are the main features of Palo.
In Baidu, the largest Chinese search engine, we run a two-tiered data warehousing system for data processing, reporting and analysis. Similar to lambda architecture, the whole data warehouse comprises data processing and data serving. Data processing does the heavy lifting of big data: cleaning data, merging and transforming it, analyzing it and preparing it for use by end user queries; data serving is designed to serve queries against that data for different use cases. Currently data processing includes batch data processing and stream data processing technology, like Hadoop, Spark and Storm; Palo is a SQL data warehouse for serving online and interactive data reporting and analysis querying.
Prior to Palo, different tools were deployed to solve diverse requirements in many ways. For example, the advertising platform needs to provide some detailed statistics associated with each served ad for every advertiser. The platform must support continuous updates, both new rows and incremental updates to existing rows within minutes. It must support latency-sensitive users serving live customer reports with very low latency requirements and batch ad-hoc multiple dimensions data analysis requiring very high throughput. In the past，this platform was built on top of sharded MySQL. But with the growth of data, MySQL cannot meet the requirements. Then, based on our existing KV system, we developed our own proprietary distributed statistical database. But, the simple KV storage was not efficient on scan performance. Because the system depends on many other systems, it is very complex to operate and maintain. Using RPC API, more complex querying usually required code programming, but users wants an MPP SQL engine. In addition to advertising system, a large number of internal BI Reporting / Analysis, also used a variety of tools. Some used the combination of SparkSQL / Impala + HDFS / HBASE. Some used MySQL to store the results that were prepared by distributed MapReduce computing. Some also bought commercial databases to use.
However, when a use case requires the simultaneous availability of capabilities that cannot all be provided by a single tool, users were forced to build hybrid architectures that stitch multiple tools together. Users often choose to ingest and update data in one storage system, but later reorganize this data to optimize for an analytical reporting use-case served from another. Our users had been successfully deploying and maintaining these hybrid architectures, but we believe that they shouldn’t need to accept their inherent complexity. A storage system built to provide great performance across a broad range of workloads provides a more elegant solution to the problems that hybrid architectures aim to solve. Palo is the solution. Palo is designed to be a simple and single tightly coupled system, not depending on other systems. Palo provides high concurrent low latency point query performance, but also provides high throughput queries of ad-hoc analysis. Palo provides bulk-batch data loading, but also provides near real-time mini-batch data loading. Palo also provides high availability, reliability, fault tolerance, and scalability.
Generally speaking, Palo is the technology combination of Google Mesa and Apache Impala. Mesa is a highly scalable analytic data storage system that stores critical measurement data related to Google's Internet advertising business. Mesa is designed to satisfy complex and challenging set of users’ and systems’ requirements, including near real-time data ingestion and query ability, as well as high availability, reliability, fault tolerance, and scalability for large data and query volumes. Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. At present, by virtue of its superior performance and rich functionality， Impala has been comparable to many commercial MPP database query engine. Mesa can satisfy the needs of many of our storage requirements, however Mesa itself does not provide a SQL query engine; Impala is a very good MPP SQL query engine, but the lack of a perfect distributed storage engine. So in the end we chose the combination of these two technologies.
Learning from Mesa’s data model, we developed a distributed storage engine. Unlike Mesa, this storage engine does not rely on any distributed file system. Then we deeply integrate this storage engine with Impala query engine. Query compiling, query execution coordination and catalog management of storage engine are integrated to be frontend daemon; query execution and data storage are integrated to be backend daemon. With this integration, we implemented a single, full-featured, high performance state the art of MPP database, as well as maintaining the simplicity.
Palo’s implementation consists of two daemons: frontend (FE) and backend (BE). The following figures gives the overview of architecture and usage.
Frontend daemon consists of query coordinator and catalog manager. Query coordinator is responsible for receiving users’ sql queries, compiling queries and managing queries execution. Catalog manager is responsible for managing metadata such as databases, tables, partitions, replicas and etc. Several frontend daemons could be deployed to guarantee fault-tolerance, and load balancing.
Backend daemon stores the data and executes the query fragments. Many backend daemons could also be deployed to provide scalability and fault-tolerance.
A typical Palo cluster generally composes of several frontend daemons and dozens to hundreds of backend daemons.
Clients can use MySQL-related tools to connect any frontend daemon to submit SQL query. The frontend receives the query and compiles it into query plans executable by the backends. Then frontend sends the query plan fragments to backend. Backends will build a query execution DAG. Data is fetched and pipelined into the DAG. The final result response is sent to client via frontend. The distribution of query fragment execution takes minimizing data movement and maximizing scan locality as the main goal. Because Palo is designed to provide interactive analysis, so the average execution time of queries is short. Considering this, we adopt query re-execution to meet the fault tolerance of query execution.
A table is splitted into many tablets. Tablets are managed by backends. The backend daemon could be configured to use multiple directories. Any directory’s IO failure doesn’t influence the normal running of backend daemon. Palo will recover and rebalance the whole cluster automatically when necessary.
In-memory catalog, multiple frontends, MySQL networking protocol, consistency guarantee, and two-level table partitioning are the main features of Palo’s frontend design.
Traditional data warehouse always uses a RDBMS database to store their catalog metadata. In order to produce query execution plan, frontend needs to look up the catalog metadata. This kind of catalog storage may be enough for low concurrent ad-hoc analysis queries. But for online high concurrent queries, its performance is very bad，resulting in increased response latency. For example, Hive metadata query latency is sometimes up to tens of seconds or even minutes. In order to speedup the metadata access, we adopt the in-memory catalog storage.
In-memory catalog storage has three functional modules: real-time memory data structures, memory checkpoints on local disk and an operation relay log. When modifying catalog, the mutation operation is written into the log file firstly. Then, the mutation operation is applied into the memory data structures. Periodically, a thread does the checkpoint that dumps memory data structure image into local disk. Checkpoint mechanism enables the fast startup of frontend and reduces the disk storage occupancy. Actually, in-memory catalog also simplifies the implementation of multiple frontends.
Many data warehouses only support single frontend-like node. There are some systems supporting master and slave deploying. But for online data serving, high availability is an essential feature. Further, the number of queries per seconds may be very large, so high scalability is also needed. In Palo, we provide the feature of multiple frontends using replicated-state-machine technology.
Frontends can be configured to three kinds of roles: leader, follower and observer. Through a voting protocol, follower frontends firstly elect a leader frontend. All the write requests of metadata are forwarded to the leader, then the leader writes the operation into the replicated log file. If the new log entry will be replicated to at least quorum followers successfully, the leader commits the operation into memory, and responses the write request. Followers always replay the replicated logs to apply them into their memory metadata. If the leader crashes, a new leader will be elected from the leftover followers. Leader and follower mainly solve the problem of write availability and partly solve the problem of read scalability.
Usually one leader frontend and several follower frontends can meet most applications’ write availability and read scalability requirements. For very high concurrent reading, continuing to increase the number of followers is not a good practice. Leader replicates log stream to followers synchronously, so adding more followers will increases write latency. Like Zookeeper，we have introduced a new type of frontend node called observer that helps addressing this problem and further improving metadata read scalability. Leader replicates log stream to observers asynchronously. Observers don’t involve leader election.
The replicated-state-machine is implemented based on BerkeleyDB java version (BDB-JE). BDB-JE has achieved high availability by implementing a Paxos-like consensus algorithm. We use BDB-JE to implement Palo’s log replication and leader election.
If a client process connects to the leader, it will see up-to-date metadata, so that strong consistency semantics is guaranteed. If the client connects to followers or observers, it will see metadata lagging a little behind of the leader, but the monotonic consistency is guaranteed. In most Palo’s use cases, monotonic consistency is accepted.
If the client always connects to the same frontend, monotonic consistency semantics is obviously guaranteed; however if the client connects to other frontends due to failover, the semantics may be violated. Palo provides a SYNC command to guarantee metadata monotonic consistency semantics during failover. When failover happens, the client can send a SYNC command to the new connected frontend, who will get the latest operation log number from the leader. The SYNC command will not return to client as long as local applied log number is still less than fetched operation log number. This mechanism can guarantee the metadata on the connected frontend is newer than the client have seen during its last connection.
MySQL compatible networking protocol is implemented in Palo’s frontend. Firstly, SQL interface is preferred for engineers; Secondly, compatibility with MySQL protocol makes the integrating with current existing BI software, such as Tableau, easier; Lastly, rich MySQL client libraries and tools reduce our development costs, but also reduces the users’ using cost.
Through the SQL interface, administrator can adjust system configuration, add and remove frontend nodes or backend nodes, and create new database for user; user can create tables, load data, and submit SQL query.
Online help document and Linux Proc-like mechanism are also supported in SQL. Users can submit queries to get the help of related SQL statements or show Palo’s internal running state.
In frontend, a small response buffer is allocated to every MySQL connection. The maximum size of this buffer is limited to 1MB. The buffer is responsible for buffering the query response data. Only if the response is finished or the buffer size reaches the 1MB，the response data will begin to be sent to client. Through this small trick, frontend can re-execution most of queries if errors occurred during query execution.
Like most of the distributed database system, data in Palo is horizontally partitioned. However, a single-level partitioning rule (hash partitioning or range partitioning) may not be a good solution to all scenarios. For example, there have a user-based fact table that stores rows of the form (date, userid, metric). Choosing only hash partitioning by column userid may lead to uneven distribution of data, when one user's data is very large. If choosing range partitioning according to column date, it will also lead to uneven distribution of data due to the likely data explosion in a certain period of time.
Therefore we support the two-level partitioning rule. The first level is range partitioning. User can specify a column (usually the time series column) range of values for the data partition. In one partition, the user can also specify one or more columns and a number of buckets to do the hash partitioning. User can combine with different partitioning rules to better divide the data. Figure 4 gives an example of two-level partitioning.
Three benefits are gained by using the two-level partitioning mechanism. Firstly, old and new data could be separated, and stored on different storage mediums; Secondly, storage engine of backend can reduce the consumption of IO and CPU for unnecessary data merging, because the data in some partitions is no longer be updated; Lastly，every partition’s buckets number can be different and adjusted according to the change of data size.
-- Create partitions using CREATE TABLE --
CREATE TABLE example_tbl (
`date` DATE,
userid BIGINT,
metric BIGINT SUM
) PARTITION BY RANGE (`date`) (
PARTITION p201601 VALUES LESS THAN ("2016-02-01"),
PARTITION p201602 VALUES LESS THAN ("2016-03-01"),
PARTITION p201603 VALUES LESS THAN ("2016-04-01"),
PARTITION p201604 VALUES LESS THAN ("2016-05-01")
) DISTRIBUTED BY HASH(userid) BUCKETS 32;
-- Add partition using ALTER TABLE --
ALTER TABLE example_tbl ADD PARTITION p201605 VALUES LESS THAN ("2016-06-01");Palo combines Google Mesa’s data model and ORCFile / Parquet storage technology.
Data in Mesa is inherently multi-dimensional fact table. These facts in table typically consist of two types of attributes: dimensional attributes (which we call keys) and measure attributes (which we call values). The table schema also specifies the aggregation function F: V ×V → V which is used to aggregate the values corresponding to the same key. To achieve high update throughput, Mesa loads data in batch. Each batch of data will be converted to a delta file. Mesa uses MVCC approach to manage these delta files, and so to enforce update atomicity. Mesa also supports creating materialized rollups, which contain a column subset of schema to gain better aggregation effect.
Mesa’s data model performs well in many interactive data service, but it also has some drawbacks:
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Users have difficulty in understanding key and value space, as well as aggregation function, especially when they rarely have such aggregation demand in analysis query scenarios.
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In order to ensure the aggregation semantic, count operation on a single column must read all columns in key space, resulting in a large number of additional read overheads. There is also unable to push down the predicates on the value column to storage engine, which also leads to additional read overheads.
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Essentially, it is still a key-value model. In order to aggregate the values corresponding to the same key, all key columns must store in order. When a table contains hundreds of columns, sorting cost becomes the bottleneck of ETL process.
To solve these problems, we introduce ORCFile / Parquet technology widely used in the open source community, such as MapReduce + ORCFile, SparkSQL + Parquet, mainly used for ad-hoc analysis of large amounts of data with low concurrency. These data does not distinguish between key and value. In addition, compared with the row-oriented database, column-oriented organization is more efficient when an aggregate needs to be computed over many rows but only for a small subset of all columns of data, because reading that smaller subset of data can be faster than reading all data. And columnar storage is also space-friendly due to the high compression ratio of each column. Further, column support block-level storage technology such as min/max index and bloom filter index. Query executor can filter out a lot of blocks that do not meet the predicate, to further improve the query performance. However, due to the underlying storage does not require data order, query time complexity is linear corresponding to the data volume.
Like traditional databases, Palo stores structured data represented as tables. Each table has a well-defined schema consisting of a finite number of columns. We combine Mesa data model and ORCFile/Parquet technology to develop a distributed analytical database. User can create two types of table to meet different needs in interactive query scenarios.
In non-aggregation type of table, columns are not distinguished between dimensions and metrics, but should specify the sort columns in order to sort all rows. Palo will sort the table data according to the sort columns without any aggregation. The following figure gives an example of creating non-aggregation table.
-- Create non-aggregation table --
CREATE TABLE example_tbl (
`date` DATE,
id BIGINT,
country VARCHAR(32),
click BIGINT,
cost BIGINT
) DUPLICATE KEY(`date`, id, country)
DISTRIBUTED BY HASH(id) BUCKETS 32;In aggregation data analysis case, we reference Mesa’s data model, and distinguish columns between key and value, and specify the value columns with aggregation method, such as SUM, REPLACE, etc. In the following figure, we create an aggregation table like the non-aggregation table, including two SUM aggregation columns (clicks, cost). Different from the non-aggregation table, data in the table needs to be sorted on all key columns for delta compaction and value aggregation.
-- Create aggregation table --
CREATE TABLE example_tbl (
`date` DATE,
id BIGINT,
country VARCHAR(32),
click BIGINT SUM,
cost BIGINT SUM
) DISTRIBUTED BY HASH(id) BUCKETS 32;Rollup is a materialized view that contains a column subset of schema in Palo. A table may contain multiple rollups with columns in different order. According to sort key index and column covering of the rollups, Palo can select the best rollup for different query. Because most rollups only contain a few columns, the size of aggregated data is typically much smaller and query performance can greatly be improved. All the rollups in the same table are updated atomically. Because rollups are materialized, users should make a trade-off between query latency and storage space when using them.
To achieve high update throughput, Palo only applies updates in batches at the smallest frequency of every minute. Each update batch specifies an increased version number and generates a delta data file, commits the version when updates of quorum replicas are complete. You can query all committed data using the committed version, and the uncommitted version would not be used in query. All update versions are strictly be in increasing order. If an update contains more than one table, the versions of these tables are committed atomically. The MVCC mechanism allows Palo to guarantee multiple table atomic updates and query consistency. In addition, Palo uses compaction policies to merge delta files to reduce delta number, also reduce the cost of delta merging during query for higher performance.
Palo’s data file is stored by column. The rows are stored in sorted order by the sort columns in delta data files, and are organized into row blocks, each block is compressed by type-specific columnar encodings, such as run-length encoding for integer columns, then stored into separate streams. In order to improve the performance of queries that have a specific key, we also store a sparse sort key index file corresponding to each delta data file. An index entry contains the short key for the row block, which is a fixed size prefix of the first sort columns for the row block, and the block id in the data file. Index files are usually directly loaded into memory, as they are very small. The algorithm for querying a specific key includes two steps. First, use a binary search on the sort key index to find blocks that may contain the specific key, and then perform a binary search on the compressed blocks in the data files to find the desired key. We also store block-level min/max index into separate index streams, and queries can use this to filter undesired blocks. In addition to those basic columnar features, we also offers an optional block-level bloom filter index for queries with IN or EQUAL conditions to further filter undesired blocks. Bloom filter index is stored in a separate stream, and is loaded on demand.
Palo applies updates in batches. Three types of data loading are supported: Hadoop-batch loading, loading ,mini-batch loading.
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Hadoop-batch loading. When a large amount of data volume needs to be loaded into Palo, the hadoop-batch loading is recommended to achieve high loading throughput. The data batches themselves are produced by an external Hadoop system, typically at a frequency of every few minutes. Unlike traditional data warehouses that use their own computing resource to do the heavy data preparation, Palo could use Hadoop to prepare the data (shuffle, sort and aggregate, etc.). By using this approach, the most time-consuming computations are handed over to Hadoop to complete. This will not only improve computational efficiency, but also reduce the performance pressure of Palo cluster and ensure the stability of the query service. The stability of the online data services is the most important point.
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Loading. After deploying the fs-brokers, you can use palo's query engine to import data. This type of loading is recommended for incremental data loading.
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Mini-batch loading. When a small amount of data needs to be loaded into Palo, the mini-batch loading is recommended to achieve low loading latency. By using http interface, raw data is pushed into a backend. Then the backend does the data preparing computing and completes the final loading. Http tools could connect frontend or backend. If frontend is connected, it will redirect the request randomly to a backend.
All the loading work is handled asynchronously. When load request is submitted, a label needs to be provided. By using the load label, users can submit show load request to get the loading status or submit cancel load request to cancel the loading. If the status of loading task is successful or in progress, its load label is not allowed to reuse again. The label of failed task is allowed to be reused.
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Multi-tenancy Isolation：Multiple virtual cluster can be created in one pysical Palo cluster. Every backend node can deploy multiple backend processes. Every backend process only belongs to one virtual cluster. Virtual cluster is one tenancy.
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User Isolation: There are many users in one virtual cluster. You can allocate the resource among different users and ensure that all users’ tasks are executed under limited resource quota.
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Priority Isolation: There are three priorities isolation group for one user. User could control resource allocated to different tasks submitted by themselves, for example user's query task and loading tasks require different resource quota.
Most machines in modern datacenter are equipped with both SSDs and HDDs. SSD has good random read capability that is the ideal medium for query that needs a large number of random read operations. However, SSD’s capacity is small and is very expensive, we could not deploy it at a large scale. HDD is cheap and has huge capacity that is suitable to store large scale data but with high read latency. In OLAP scenario, we find user usually submit a lot of queries to query the latest data (hot data) and expect low latency. User occasionally executes query on historical data (cold data). This kind of query usually needs to scan large scale of data and is high latency. Multi-Medium Storage allows users to manage the storage medium of the data to meet different query scenarios and reduce the latency. For example, user could put latest data on SSD and historical data which is not used frequently on HDD, user will get low latency when querying latest data while get high latency when query historical data which is normal because it needs scan large scale data.
In the following figure, user alters partition 'p201601' storage_medium to SSD and storage_cooldown_time to '2016-07-01 00:00:00'. The setting means data in this partition will be put on SSD and it will start to migrate to HDD after the time of storage_cooldown_time.
ALTER TABLE example_tbl MODIFY PARTITION p201601
SET ("storage_medium" = "SSD", "storage_cooldown_time" = "2016-07-01 00:00:00");Runtime code generation using LLVM is one of the techniques employed extensively by Impala to improve query execution times. Performance could gains of 5X or more are typical for representative workloads.
But, runtime code generation is not suitable for low latency query, because the generation overhead costs about 100ms. Runtime code generation is more suitable for large-scale ad-hoc query. To accelerate the small queries (of course, big queries will also obtain benefits), we introduced vectorized query execution into Palo.
Vectorized query execution is a feature that greatly reduces the CPU usage for typical query operations like scans, filters, aggregates, and joins. A standard query execution system processes one row at a time. This involves long code paths and significant metadata interpretation in the inner loop of execution. Vectorized query execution streamlines operations by processing a block of many rows at a time. Within the block, each column is stored as a vector (an array of a primitive data type). Simple operations like arithmetic and comparisons are done by quickly iterating through the vectors in a tight loop, with no or very few function calls or conditional branches inside the loop. These loops compile in a streamlined way that uses relatively few instructions and finishes each instruction in fewer clock cycles, on average, by effectively using the processor pipeline and cache memory.
The result of benchmark shows 2x~4x speedup in our typical queries.
Data backup function is provided to enhance data security. The minimum granularity of backup and recovery is partition. Users can develop plugins to backup data to any specified remote storage. The backup data can always be recovered to Palo at all time, to achieve the data rollback purpose.
Currently we only support full data backup data rather than incremental backups for the following reasons:
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Remote storage system is beyond the control of the Palo system. We cannot guarantee whether the data has been changed between two backup operations. And data verification operations always come at a high price.
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We support data backup on partition granularity. And majority of applications are time series applications. By dividing data using time column, it has been able to meet the needs of the vast majority of incremental backup in chronological order.
In addition to improving data security, the backup function also provides a way to export the data. Data can be exported to other downstream systems for further processing.
Palo only supports Linux System. Oracle JDK 8.0+ and GCC 4.8.2+ are required. See the document of INSTALL and Deploy & Update
See the Palo Wiki for more information.
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